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Release Notes

Future Releases
  • Enhancements
  • Fixes
  • Changes
    • Removed DeprecationWarning for SimpleImputer :pr:`1018`
  • Documentation Changes
  • Testing Changes
v0.12.0 Aug. 3, 2020
  • Enhancements
    • Added string and categorical targets support for binary and multiclass pipelines and check for numeric targets for DetectLabelLeakage data check :pr:`932`
    • Added clear exception for regression pipelines if target datatype is string or categorical :pr:`960`
    • Added target column names and class labels in predict and predict_proba output for pipelines :pr:`951`
    • Added _compute_shap_values and normalize_values to pipelines/explanations module :pr:`958`
    • Added explain_prediction feature which explains single predictions with SHAP :pr:`974`
    • Added Imputer to allow different imputation strategies for numerical and categorical dtypes :pr:`991`
    • Added support for configuring logfile path using env var, and don't create logger if there are filesystem errors :pr:`975`
    • Updated catboost estimators' default parameters and automl hyperparameter ranges to speed up fit time :pr:`998`
  • Fixes
    • Fixed ReadtheDocs warning failure regarding embedded gif :pr:`943`
    • Removed incorrect parameter passed to pipeline classes in _add_baseline_pipelines :pr:`941`
    • Added universal error for calling predict, predict_proba, transform, and feature_importances before fitting :pr:`969`, :pr:`994`
    • Made TextFeaturizer component and pip dependencies featuretools and nlp_primitives optional :pr:`976`
    • Updated imputation strategy in automl to no longer limit impute strategy to most_frequent for all features if there are any categorical columns :pr:`991`
    • Fixed UnboundLocalError for`cv_pipeline` when automl search errors :pr:`996`
    • Fixed Imputer to reset dataframe index to preserve behavior expected from SimpleImputer :pr:`1009`
  • Changes
    • Moved get_estimators ` to `evalml.pipelines.components.utils :pr:`934`
    • Modified Pipelines to raise PipelineScoreError when they encounter an error during scoring :pr:`936`
    • Moved evalml.model_families.list_model_families to evalml.pipelines.components.allowed_model_families :pr:`959`
    • Renamed DateTimeFeaturization to DateTimeFeaturizer :pr:`977`
    • Added check to stop search and raise an error if all pipelines in a batch return NaN scores :pr:`1015`
  • Documentation Changes
    • Update README.md :pr:`963`
    • Reworded message when errors are returned from data checks in search :pr:`982`
    • Added section on understanding model predictions with explain_prediction to User Guide :pr:`981`
    • Added a section to the user guide and api reference about how XGBoost and CatBoost are not fully supported. :pr:`992`
    • Added custom components section in user guide :pr:`993`
    • Update FAQ section formatting :pr:`997`
    • Update release process documentation :pr:`1003`
  • Testing Changes
    • Moved predict_proba and predict tests regarding string / categorical targets to test_pipelines.py :pr:`972`
    • Fix dependency update bot by updating python version to 3.7 to avoid frequent github version updates :pr:`1002`

Warning

Breaking Changes
  • get_estimators has been moved to evalml.pipelines.components.utils (previously was under evalml.pipelines.utils) :pr:`934`
  • Removed the raise_errors flag in AutoML search. All errors during pipeline evaluation will be caught and logged. :pr:`936`
  • evalml.model_families.list_model_families has been moved to evalml.pipelines.components.allowed_model_families :pr:`959`
  • TextFeaturizer: the featuretools and nlp_primitives packages must be installed after installing evalml in order to use this component :pr:`976`
  • Renamed DateTimeFeaturization to DateTimeFeaturizer :pr:`977`
v0.11.2 July 16, 2020
  • Enhancements
    • Added NoVarianceDataCheck to DefaultDataChecks :pr:`893`
    • Added text processing and featurization component TextFeaturizer :pr:`913`, :pr:`924`
    • Added additional checks to InvalidTargetDataCheck to handle invalid target data types :pr:`929`
    • AutoMLSearch will now handle KeyboardInterrupt and prompt user for confirmation :pr:`915`
  • Fixes
    • Makes automl results a read-only property :pr:`919`
  • Changes
    • Deleted static pipelines and refactored tests involving static pipelines, removed all_pipelines() and get_pipelines() :pr:`904`
    • Moved list_model_families to evalml.model_family.utils :pr:`903`
    • Updated all_pipelines, all_estimators, all_components to use the same mechanism for dynamically generating their elements :pr:`898`
    • Rename master branch to main :pr:`918`
    • Add pypi release github action :pr:`923`
    • Updated AutoMLSearch.search stdout output and logging and removed tqdm progress bar :pr:`921`
    • Moved automl config checks previously in search() to init :pr:`933`
  • Documentation Changes
    • Reorganized and rewrote documentation :pr:`937`
    • Updated to use pydata sphinx theme :pr:`937`
    • Updated docs to use release_notes instead of changelog :pr:`942`
  • Testing Changes
    • Cleaned up fixture names and usages in tests :pr:`895`

Warning

Breaking Changes
  • list_model_families has been moved to evalml.model_family.utils (previously was under evalml.pipelines.utils) :pr:`903`
  • get_estimators has been moved to evalml.pipelines.components.utils (previously was under evalml.pipelines.utils) :pr:`934`
  • Static pipeline definitions have been removed, but similar pipelines can still be constructed via creating an instance of PipelineBase :pr:`904`
  • all_pipelines() and get_pipelines() utility methods have been removed :pr:`904`
v0.11.0 June 30, 2020
  • Enhancements
    • Added multiclass support for ROC curve graphing :pr:`832`
    • Added preprocessing component to drop features whose percentage of NaN values exceeds a specified threshold :pr:`834`
    • Added data check to check for problematic target labels :pr:`814`
    • Added PerColumnImputer that allows imputation strategies per column :pr:`824`
    • Added transformer to drop specific columns :pr:`827`
    • Added support for categories, handle_error, and drop parameters in OneHotEncoder :pr:`830` :pr:`897`
    • Added preprocessing component to handle DateTime columns featurization :pr:`838`
    • Added ability to clone pipelines and components :pr:`842`
    • Define getter method for component parameters :pr:`847`
    • Added utility methods to calculate and graph permutation importances :pr:`860`, :pr:`880`
    • Added new utility functions necessary for generating dynamic preprocessing pipelines :pr:`852`
    • Added kwargs to all components :pr:`863`
    • Updated AutoSearchBase to use dynamically generated preprocessing pipelines :pr:`870`
    • Added SelectColumns transformer :pr:`873`
    • Added ability to evaluate additional pipelines for automl search :pr:`874`
    • Added default_parameters class property to components and pipelines :pr:`879`
    • Added better support for disabling data checks in automl search :pr:`892`
    • Added ability to save and load AutoML objects to file :pr:`888`
    • Updated AutoSearchBase.get_pipelines to return an untrained pipeline instance :pr:`876`
    • Saved learned binary classification thresholds in automl results cv data dict :pr:`876`
  • Fixes
    • Fixed bug where SimpleImputer cannot handle dropped columns :pr:`846`
    • Fixed bug where PerColumnImputer cannot handle dropped columns :pr:`855`
    • Enforce requirement that builtin components save all inputted values in their parameters dict :pr:`847`
    • Don't list base classes in all_components output :pr:`847`
    • Standardize all components to output pandas data structures, and accept either pandas or numpy :pr:`853`
    • Fixed rankings and full_rankings error when search has not been run :pr:`894`
  • Changes
    • Update all_pipelines and all_components to try initializing pipelines/components, and on failure exclude them :pr:`849`
    • Refactor handle_components to handle_components_class, standardize to ComponentBase subclass instead of instance :pr:`850`
    • Refactor "blacklist"/"whitelist" to "allow"/"exclude" lists :pr:`854`
    • Replaced AutoClassificationSearch and AutoRegressionSearch with AutoMLSearch :pr:`871`
    • Renamed feature_importances and permutation_importances methods to use singular names (feature_importance and permutation_importance) :pr:`883`
    • Updated automl default data splitter to train/validation split for large datasets :pr:`877`
    • Added open source license, update some repo metadata :pr:`887`
    • Removed dead code in _get_preprocessing_components :pr:`896`
  • Documentation Changes
    • Fix some typos and update the EvalML logo :pr:`872`
  • Testing Changes
    • Update the changelog check job to expect the new branching pattern for the deps update bot :pr:`836`
    • Check that all components output pandas datastructures, and can accept either pandas or numpy :pr:`853`
    • Replaced AutoClassificationSearch and AutoRegressionSearch with AutoMLSearch :pr:`871`

Warning

Breaking Changes
  • Pipelines' static component_graph field must contain either ComponentBase subclasses or str, instead of ComponentBase subclass instances :pr:`850`
  • Rename handle_component to handle_component_class. Now standardizes to ComponentBase subclasses instead of ComponentBase subclass instances :pr:`850`
  • Renamed automl's cv argument to data_split :pr:`877`
  • Pipelines' and classifiers' feature_importances is renamed feature_importance, graph_feature_importances is renamed graph_feature_importance :pr:`883`
  • Passing data_checks=None to automl search will not perform any data checks as opposed to default checks. :pr:`892`
  • Pipelines to search for in AutoML are now determined automatically, rather than using the statically-defined pipeline classes. :pr:`870`
  • Updated AutoSearchBase.get_pipelines to return an untrained pipeline instance, instead of one which happened to be trained on the final cross-validation fold :pr:`876`
v0.10.0 May 29, 2020
  • Enhancements
    • Added baseline models for classification and regression, add functionality to calculate baseline models before searching in AutoML :pr:`746`
    • Port over highly-null guardrail as a data check and define DefaultDataChecks and DisableDataChecks classes :pr:`745`
    • Update Tuner classes to work directly with pipeline parameters dicts instead of flat parameter lists :pr:`779`
    • Add Elastic Net as a pipeline option :pr:`812`
    • Added new Pipeline option ExtraTrees :pr:`790`
    • Added precicion-recall curve metrics and plot for binary classification problems in evalml.pipeline.graph_utils :pr:`794`
    • Update the default automl algorithm to search in batches, starting with default parameters for each pipeline and iterating from there :pr:`793`
    • Added AutoMLAlgorithm class and IterativeAlgorithm impl, separated from AutoSearchBase :pr:`793`
  • Fixes
    • Update pipeline score to return nan score for any objective which throws an exception during scoring :pr:`787`
    • Fixed bug introduced in :pr:`787` where binary classification metrics requiring predicted probabilities error in scoring :pr:`798`
    • CatBoost and XGBoost classifiers and regressors can no longer have a learning rate of 0 :pr:`795`
  • Changes
    • Cleanup pipeline score code, and cleanup codecov :pr:`711`
    • Remove pass for abstract methods for codecov :pr:`730`
    • Added __str__ for AutoSearch object :pr:`675`
    • Add util methods to graph ROC and confusion matrix :pr:`720`
    • Refactor AutoBase to AutoSearchBase :pr:`758`
    • Updated AutoBase with data_checks parameter, removed previous detect_label_leakage parameter, and added functionality to run data checks before search in AutoML :pr:`765`
    • Updated our logger to use Python's logging utils :pr:`763`
    • Refactor most of AutoSearchBase._do_iteration impl into AutoSearchBase._evaluate :pr:`762`
    • Port over all guardrails to use the new DataCheck API :pr:`789`
    • Expanded import_or_raise to catch all exceptions :pr:`759`
    • Adds RMSE, MSLE, RMSLE as standard metrics :pr:`788`
    • Don't allow Recall to be used as an objective for AutoML :pr:`784`
    • Removed feature selection from pipelines :pr:`819`
    • Update default estimator parameters to make automl search faster and more accurate :pr:`793`
  • Documentation Changes
    • Add instructions to freeze master on release.md :pr:`726`
    • Update release instructions with more details :pr:`727` :pr:`733`
    • Add objective base classes to API reference :pr:`736`
    • Fix components API to match other modules :pr:`747`
  • Testing Changes
    • Delete codecov yml, use codecov.io's default :pr:`732`
    • Added unit tests for fraud cost, lead scoring, and standard metric objectives :pr:`741`
    • Update codecov client :pr:`782`
    • Updated AutoBase __str__ test to include no parameters case :pr:`783`
    • Added unit tests for ExtraTrees pipeline :pr:`790`
    • If codecov fails to upload, fail build :pr:`810`
    • Updated Python version of dependency action :pr:`816`
    • Update the dependency update bot to use a suffix when creating branches :pr:`817`

Warning

Breaking Changes
  • The detect_label_leakage parameter for AutoML classes has been removed and replaced by a data_checks parameter :pr:`765`
  • Moved ROC and confusion matrix methods from evalml.pipeline.plot_utils to evalml.pipeline.graph_utils :pr:`720`
  • Tuner classes require a pipeline hyperparameter range dict as an init arg instead of a space definition :pr:`779`
  • Tuner.propose and Tuner.add work directly with pipeline parameters dicts instead of flat parameter lists :pr:`779`
  • PipelineBase.hyperparameters and custom_hyperparameters use pipeline parameters dict format instead of being represented as a flat list :pr:`779`
  • All guardrail functions previously under evalml.guardrails.utils will be removed and replaced by data checks :pr:`789`
  • Recall disallowed as an objective for AutoML :pr:`784`
  • AutoSearchBase parameter tuner has been renamed to tuner_class :pr:`793`
  • AutoSearchBase parameter possible_pipelines and possible_model_families have been renamed to allowed_pipelines and allowed_model_families :pr:`793`
v0.9.0 Apr. 27, 2020
  • Enhancements
    • Added accuracy as an standard objective :pr:`624`
    • Added verbose parameter to load_fraud :pr:`560`
    • Added Balanced Accuracy metric for binary, multiclass :pr:`612` :pr:`661`
    • Added XGBoost regressor and XGBoost regression pipeline :pr:`666`
    • Added Accuracy metric for multiclass :pr:`672`
    • Added objective name in AutoBase.describe_pipeline :pr:`686`
    • Added DataCheck and DataChecks, Message classes and relevant subclasses :pr:`739`
  • Fixes
    • Removed direct access to cls.component_graph :pr:`595`
    • Add testing files to .gitignore :pr:`625`
    • Remove circular dependencies from Makefile :pr:`637`
    • Add error case for normalize_confusion_matrix() :pr:`640`
    • Fixed XGBoostClassifier and XGBoostRegressor bug with feature names that contain [, ], or < :pr:`659`
    • Update make_pipeline_graph to not accidentally create empty file when testing if path is valid :pr:`649`
    • Fix pip installation warning about docsutils version, from boto dependency :pr:`664`
    • Removed zero division warning for F1/precision/recall metrics :pr:`671`
    • Fixed summary for pipelines without estimators :pr:`707`
  • Changes
    • Updated default objective for binary/multiseries classification to log loss :pr:`613`
    • Created classification and regression pipeline subclasses and removed objective as an attribute of pipeline classes :pr:`405`
    • Changed the output of score to return one dictionary :pr:`429`
    • Created binary and multiclass objective subclasses :pr:`504`
    • Updated objectives API :pr:`445`
    • Removed call to get_plot_data from AutoML :pr:`615`
    • Set raise_error to default to True for AutoML classes :pr:`638`
    • Remove unnecessary "u" prefixes on some unicode strings :pr:`641`
    • Changed one-hot encoder to return uint8 dtypes instead of ints :pr:`653`
    • Pipeline _name field changed to custom_name :pr:`650`
    • Removed graphs.py and moved methods into PipelineBase :pr:`657`, :pr:`665`
    • Remove s3fs as a dev dependency :pr:`664`
    • Changed requirements-parser to be a core dependency :pr:`673`
    • Replace supported_problem_types field on pipelines with problem_type attribute on base classes :pr:`678`
    • Changed AutoML to only show best results for a given pipeline template in rankings, added full_rankings property to show all :pr:`682`
    • Update ModelFamily values: don't list xgboost/catboost as classifiers now that we have regression pipelines for them :pr:`677`
    • Changed AutoML's describe_pipeline to get problem type from pipeline instead :pr:`685`
    • Standardize import_or_raise error messages :pr:`683`
    • Updated argument order of objectives to align with sklearn's :pr:`698`
    • Renamed pipeline.feature_importance_graph to pipeline.graph_feature_importances :pr:`700`
    • Moved ROC and confusion matrix methods to evalml.pipelines.plot_utils :pr:`704`
    • Renamed MultiClassificationObjective to MulticlassClassificationObjective, to align with pipeline naming scheme :pr:`715`
  • Documentation Changes
    • Fixed some sphinx warnings :pr:`593`
    • Fixed docstring for AutoClassificationSearch with correct command :pr:`599`
    • Limit readthedocs formats to pdf, not htmlzip and epub :pr:`594` :pr:`600`
    • Clean up objectives API documentation :pr:`605`
    • Fixed function on Exploring search results page :pr:`604`
    • Update release process doc :pr:`567`
    • AutoClassificationSearch and AutoRegressionSearch show inherited methods in API reference :pr:`651`
    • Fixed improperly formatted code in breaking changes for changelog :pr:`655`
    • Added configuration to treat Sphinx warnings as errors :pr:`660`
    • Removed separate plotting section for pipelines in API reference :pr:`657`, :pr:`665`
    • Have leads example notebook load S3 files using https, so we can delete s3fs dev dependency :pr:`664`
    • Categorized components in API reference and added descriptions for each category :pr:`663`
    • Fixed Sphinx warnings about BalancedAccuracy objective :pr:`669`
    • Updated API reference to include missing components and clean up pipeline docstrings :pr:`689`
    • Reorganize API ref, and clarify pipeline sub-titles :pr:`688`
    • Add and update preprocessing utils in API reference :pr:`687`
    • Added inheritance diagrams to API reference :pr:`695`
    • Documented which default objective AutoML optimizes for :pr:`699`
    • Create seperate install page :pr:`701`
    • Include more utils in API ref, like import_or_raise :pr:`704`
    • Add more color to pipeline documentation :pr:`705`
  • Testing Changes
    • Matched install commands of check_latest_dependencies test and it's GitHub action :pr:`578`
    • Added Github app to auto assign PR author as assignee :pr:`477`
    • Removed unneeded conda installation of xgboost in windows checkin tests :pr:`618`
    • Update graph tests to always use tmpfile dir :pr:`649`
    • Changelog checkin test workaround for release PRs: If 'future release' section is empty of PR refs, pass check :pr:`658`
    • Add changelog checkin test exception for dep-update branch :pr:`723`

Warning

Breaking Changes

  • Pipelines will now no longer take an objective parameter during instantiation, and will no longer have an objective attribute.
  • fit() and predict() now use an optional objective parameter, which is only used in binary classification pipelines to fit for a specific objective.
  • score() will now use a required objectives parameter that is used to determine all the objectives to score on. This differs from the previous behavior, where the pipeline's objective was scored on regardless.
  • score() will now return one dictionary of all objective scores.
  • ROC and ConfusionMatrix plot methods via Auto(*).plot have been removed by :pr:`615` and are replaced by roc_curve and confusion_matrix in evamlm.pipelines.plot_utils` in :pr:`704`
  • normalize_confusion_matrix has been moved to evalml.pipelines.plot_utils :pr:`704`
  • Pipelines _name field changed to custom_name
  • Pipelines supported_problem_types field is removed because it is no longer necessary :pr:`678`
  • Updated argument order of objectives' objective_function to align with sklearn :pr:`698`
  • pipeline.feature_importance_graph has been renamed to pipeline.graph_feature_importances in :pr:`700`
  • Removed unsupported MSLE objective :pr:`704`
v0.8.0 Apr. 1, 2020
  • Enhancements
    • Add normalization option and information to confusion matrix :pr:`484`
    • Add util function to drop rows with NaN values :pr:`487`
    • Renamed PipelineBase.name as PipelineBase.summary and redefined PipelineBase.name as class property :pr:`491`
    • Added access to parameters in Pipelines with PipelineBase.parameters (used to be return of PipelineBase.describe) :pr:`501`
    • Added fill_value parameter for SimpleImputer :pr:`509`
    • Added functionality to override component hyperparameters and made pipelines take hyperparemeters from components :pr:`516`
    • Allow numpy.random.RandomState for random_state parameters :pr:`556`
  • Fixes
    • Removed unused dependency matplotlib, and move category_encoders to test reqs :pr:`572`
  • Changes
    • Undo version cap in XGBoost placed in :pr:`402` and allowed all released of XGBoost :pr:`407`
    • Support pandas 1.0.0 :pr:`486`
    • Made all references to the logger static :pr:`503`
    • Refactored model_type parameter for components and pipelines to model_family :pr:`507`
    • Refactored problem_types for pipelines and components into supported_problem_types :pr:`515`
    • Moved pipelines/utils.save_pipeline and pipelines/utils.load_pipeline to PipelineBase.save and PipelineBase.load :pr:`526`
    • Limit number of categories encoded by OneHotEncoder :pr:`517`
  • Documentation Changes
    • Updated API reference to remove PipelinePlot and added moved PipelineBase plotting methods :pr:`483`
    • Add code style and github issue guides :pr:`463` :pr:`512`
    • Updated API reference for to surface class variables for pipelines and components :pr:`537`
    • Fixed README documentation link :pr:`535`
    • Unhid PR references in changelog :pr:`656`
  • Testing Changes
    • Added automated dependency check PR :pr:`482`, :pr:`505`
    • Updated automated dependency check comment :pr:`497`
    • Have build_docs job use python executor, so that env vars are set properly :pr:`547`
    • Added simple test to make sure OneHotEncoder's top_n works with large number of categories :pr:`552`
    • Run windows unit tests on PRs :pr:`557`

Warning

Breaking Changes

  • AutoClassificationSearch and AutoRegressionSearch's model_types parameter has been refactored into allowed_model_families
  • ModelTypes enum has been changed to ModelFamily
  • Components and Pipelines now have a model_family field instead of model_type
  • get_pipelines utility function now accepts model_families as an argument instead of model_types
  • PipelineBase.name no longer returns structure of pipeline and has been replaced by PipelineBase.summary
  • PipelineBase.problem_types and Estimator.problem_types has been renamed to supported_problem_types
  • pipelines/utils.save_pipeline and pipelines/utils.load_pipeline moved to PipelineBase.save and PipelineBase.load
v0.7.0 Mar. 9, 2020
  • Enhancements
    • Added emacs buffers to .gitignore :pr:`350`
    • Add CatBoost (gradient-boosted trees) classification and regression components and pipelines :pr:`247`
    • Added Tuner abstract base class :pr:`351`
    • Added n_jobs as parameter for AutoClassificationSearch and AutoRegressionSearch :pr:`403`
    • Changed colors of confusion matrix to shades of blue and updated axis order to match scikit-learn's :pr:`426`
    • Added PipelineBase graph and feature_importance_graph methods, moved from previous location :pr:`423`
    • Added support for python 3.8 :pr:`462`
  • Fixes
    • Fixed ROC and confusion matrix plots not being calculated if user passed own additional_objectives :pr:`276`
    • Fixed ReadtheDocs FileNotFoundError exception for fraud dataset :pr:`439`
  • Changes
    • Added n_estimators as a tunable parameter for XGBoost :pr:`307`
    • Remove unused parameter ObjectiveBase.fit_needs_proba :pr:`320`
    • Remove extraneous parameter component_type from all components :pr:`361`
    • Remove unused rankings.csv file :pr:`397`
    • Downloaded demo and test datasets so unit tests can run offline :pr:`408`
    • Remove _needs_fitting attribute from Components :pr:`398`
    • Changed plot.feature_importance to show only non-zero feature importances by default, added optional parameter to show all :pr:`413`
    • Refactored PipelineBase to take in parameter dictionary and moved pipeline metadata to class attribute :pr:`421`
    • Dropped support for Python 3.5 :pr:`438`
    • Removed unused apply.py file :pr:`449`
    • Clean up requirements.txt to remove unused deps :pr:`451`
    • Support installation without all required dependencies :pr:`459`
  • Documentation Changes
    • Update release.md with instructions to release to internal license key :pr:`354`
  • Testing Changes
    • Added tests for utils (and moved current utils to gen_utils) :pr:`297`
    • Moved XGBoost install into it's own separate step on Windows using Conda :pr:`313`
    • Rewind pandas version to before 1.0.0, to diagnose test failures for that version :pr:`325`
    • Added dependency update checkin test :pr:`324`
    • Rewind XGBoost version to before 1.0.0 to diagnose test failures for that version :pr:`402`
    • Update dependency check to use a whitelist :pr:`417`
    • Update unit test jobs to not install dev deps :pr:`455`

Warning

Breaking Changes

  • Python 3.5 will not be actively supported.
v0.6.0 Dec. 16, 2019
  • Enhancements
    • Added ability to create a plot of feature importances :pr:`133`
    • Add early stopping to AutoML using patience and tolerance parameters :pr:`241`
    • Added ROC and confusion matrix metrics and plot for classification problems and introduce PipelineSearchPlots class :pr:`242`
    • Enhanced AutoML results with search order :pr:`260`
    • Added utility function to show system and environment information :pr:`300`
  • Fixes
    • Lower botocore requirement :pr:`235`
    • Fixed decision_function calculation for FraudCost objective :pr:`254`
    • Fixed return value of Recall metrics :pr:`264`
    • Components return self on fit :pr:`289`
  • Changes
    • Renamed automl classes to AutoRegressionSearch and AutoClassificationSearch :pr:`287`
    • Updating demo datasets to retain column names :pr:`223`
    • Moving pipeline visualization to PipelinePlots class :pr:`228`
    • Standarizing inputs as pd.Dataframe / pd.Series :pr:`130`
    • Enforcing that pipelines must have an estimator as last component :pr:`277`
    • Added ipywidgets as a dependency in requirements.txt :pr:`278`
    • Added Random and Grid Search Tuners :pr:`240`
  • Documentation Changes
    • Adding class properties to API reference :pr:`244`
    • Fix and filter FutureWarnings from scikit-learn :pr:`249`, :pr:`257`
    • Adding Linear Regression to API reference and cleaning up some Sphinx warnings :pr:`227`
  • Testing Changes
    • Added support for testing on Windows with CircleCI :pr:`226`
    • Added support for doctests :pr:`233`

Warning

Breaking Changes

  • The fit() method for AutoClassifier and AutoRegressor has been renamed to search().
  • AutoClassifier has been renamed to AutoClassificationSearch
  • AutoRegressor has been renamed to AutoRegressionSearch
  • AutoClassificationSearch.results and AutoRegressionSearch.results now is a dictionary with pipeline_results and search_order keys. pipeline_results can be used to access a dictionary that is identical to the old .results dictionary. Whereas, search_order returns a list of the search order in terms of pipeline_id.
  • Pipelines now require an estimator as the last component in component_list. Slicing pipelines now throws an NotImplementedError to avoid returning pipelines without an estimator.
v0.5.2 Nov. 18, 2019
  • Enhancements
    • Adding basic pipeline structure visualization :pr:`211`
  • Documentation Changes
v0.5.1 Nov. 15, 2019
  • Enhancements
    • Added basic outlier detection guardrail :pr:`151`
    • Added basic ID column guardrail :pr:`135`
    • Added support for unlimited pipelines with a max_time limit :pr:`70`
    • Updated .readthedocs.yaml to successfully build :pr:`188`
  • Fixes
    • Removed MSLE from default additional objectives :pr:`203`
    • Fixed random_state passed in pipelines :pr:`204`
    • Fixed slow down in RFRegressor :pr:`206`
  • Changes
  • Documentation Changes
v0.5.0 Oct. 29, 2019
  • Enhancements
    • Added basic one hot encoding :pr:`73`
    • Use enums for model_type :pr:`110`
    • Support for splitting regression datasets :pr:`112`
    • Auto-infer multiclass classification :pr:`99`
    • Added support for other units in max_time :pr:`125`
    • Detect highly null columns :pr:`121`
    • Added additional regression objectives :pr:`100`
    • Show an interactive iteration vs. score plot when using fit() :pr:`134`
  • Fixes
    • Reordered describe_pipeline :pr:`94`
    • Added type check for model_type :pr:`109`
    • Fixed s units when setting string max_time :pr:`132`
    • Fix objectives not appearing in API documentation :pr:`150`
  • Changes
    • Reorganized tests :pr:`93`
    • Moved logging to its own module :pr:`119`
    • Show progress bar history :pr:`111`
    • Using cloudpickle instead of pickle to allow unloading of custom objectives :pr:`113`
    • Removed render.py :pr:`154`
  • Documentation Changes
  • Testing Changes
    • Code coverage :pr:`90`
    • Added CircleCI tests for other Python versions :pr:`104`
    • Added doc notebooks as tests :pr:`139`
    • Test metadata for CircleCI and 2 core parallelism :pr:`137`
v0.4.1 Sep. 16, 2019
  • Enhancements
    • Added AutoML for classification and regressor using Autobase and Skopt :pr:`7` :pr:`9`
    • Implemented standard classification and regression metrics :pr:`7`
    • Added logistic regression, random forest, and XGBoost pipelines :pr:`7`
    • Implemented support for custom objectives :pr:`15`
    • Feature importance for pipelines :pr:`18`
    • Serialization for pipelines :pr:`19`
    • Allow fitting on objectives for optimal threshold :pr:`27`
    • Added detect label leakage :pr:`31`
    • Implemented callbacks :pr:`42`
    • Allow for multiclass classification :pr:`21`
    • Added support for additional objectives :pr:`79`
  • Fixes
    • Fixed feature selection in pipelines :pr:`13`
    • Made random_seed usage consistent :pr:`45`
  • Documentation Changes
    • Documentation Changes
    • Added docstrings :pr:`6`
    • Created notebooks for docs :pr:`6`
    • Initialized readthedocs EvalML :pr:`6`
    • Added favicon :pr:`38`
  • Testing Changes
    • Added testing for loading data :pr:`39`
v0.2.0 Aug. 13, 2019
  • Enhancements
    • Created fraud detection objective :pr:`4`
v0.1.0 July. 31, 2019
  • First Release
  • Enhancements
  • Documentation Changes
    • Initialized Sphinx for docs :pr:`1`