Enhancements
Fixes
Changes
Documentation Changes
Testing Changes
Added output_format field to explain predictions functions #1107
Modified get_objective and get_objectives to be able to return any objective in evalml.objectives #1132
Added a return_instance boolean parameter to get_objective #1132
Added ClassImbalanceDataCheck to determine whether target imbalance falls below a given threshold #1135
Added label encoder to lightGBM for binary classification #1152
Added labels for the row index of confusion matrix #1154
Added AutoMLSearch object as another parameter in search callbacks #1156
Added the corresponding probability threshold for each point displayed in graph_roc_curve #1161
Added __eq__ for ComponentBase and PipelineBase #1178
Added support for multiclass classification for roc_curve #1164
Added categories accessor to OneHotEncoder for listing the categories associated with a feature #1182
Added utility function to create pipeline instances from a list of component instances #1176
Fixed XGBoost column names for partial dependence methods #1104
Removed dead code validating column type from TextFeaturizer #1122
Fixed issue where Imputer cannot fit when there is None in a categorical or boolean column #1144
OneHotEncoder preserves the custom index in the input data #1146
Fixed representation for ModelFamily #1165
Removed duplicate nbsphinx dependency in dev-requirements.txt #1168
Users can now pass in any valid kwargs to all estimators #1157
Remove broken accessor OneHotEncoder.get_feature_names and unneeded base class #1179
Removed LightGBM Estimator from AutoML models #1186
Pinned scikit-optimize version to 0.7.4 #1136
Removed tqdm as a dependency #1177
Added lightgbm version 3.0.0 to latest_dependency_versions.txt #1185
Fixed API docs for AutoMLSearch add_result_callback #1113
Added a step to our release process for pushing our latest version to conda-forge #1118
Added warning for missing ipywidgets dependency for using PipelineSearchPlots on Jupyterlab #1145
Updated README.md example to load demo dataset #1151
Swapped mapping of breast cancer targets in model_understanding.ipynb #1170
Added test confirming TextFeaturizer never outputs null values #1122
Changed Python version of Update Dependencies action to 3.8.x #1137
Fixed release notes check-in test for Update Dependencies actions #1172
Warning
get_objective will now return a class definition rather than an instance by default #1132
Deleted OPTIONS dictionary in evalml.objectives.utils.py #1132
If specifying an objective by string, the string must now match the objective’s name field, case-insensitive #1132
“Recall”, “Recall Macro”, “Recall Micro”, “Recall Weighted”, or “Root Mean Squared Log Error” to AutoMLSearch will now result in a ValueError rather than an ObjectiveNotFoundError #1132
Search callbacks start_iteration_callback and add_results_callback have changed to include a copy of the AutoMLSearch object as a third parameter #1156
Deleted OneHotEncoder.get_feature_names method which had been broken for a while, in favor of pipelines’ input_feature_names #1179
Deleted empty base class CategoricalEncoder which OneHotEncoder component was inheriting from #1176
Results from roc_curve will now return as a list of dictionaries with each dictionary representing a class #1164
Added Cost-Benefit Matrix objective for binary classification #1038
Split fill_value into categorical_fill_value and numeric_fill_value for Imputer #1019
Added explain_predictions and explain_predictions_best_worst for explaining multiple predictions with SHAP #1016
Added new LSA component for text featurization #1022
Added guide on installing with conda #1041
Added a “cost-benefit curve” util method to graph cost-benefit matrix scores vs. binary classification thresholds #1081
Standardized error when calling transform/predict before fit for pipelines #1048
Added percent_better_than_baseline to Automl search rankings and full rankings table #1050
Added one-way partial dependence and partial dependence plots #1079
Added “Feature Value” column to prediction explanation reports. #1064
Added LightGBM classification estimator #1082, #1114
Added max_batches parameter to AutoMLSearch #1087
Updated TextFeaturizer component to no longer require an internet connection to run #1022
Fixed non-deterministic element of TextFeaturizer transformations #1022
Added a StandardScaler to all ElasticNet pipelines #1065
Updated cost-benefit matrix to normalize score #1099
Fixed logic in calculate_percent_difference so that it can handle negative values #1100
Added needs_fitting property to ComponentBase #1044
Updated references to data types to use datatype lists defined in evalml.utils.gen_utils #1039
Remove maximum version limit for SciPy dependency #1051
Moved all_components and other component importers into runtime methods #1045
Consolidated graphing utility methods under evalml.utils.graph_utils #1060
Made slight tweaks to how TextFeaturizer uses featuretools, and did some refactoring of that and of LSA #1090
Changed show_all_features parameter into importance_threshold, which allows for thresholding feature importance #1097, #1103
Update setup.py URL to point to the github repo #1037
Added tutorial for using the cost-benefit matrix objective #1088
Updated model_understanding.ipynb to include documentation for using plotly on Jupyter Lab #1108
Refactor CircleCI tests to use matrix jobs (#1043)
Added a test to check that all test directories are included in evalml package #1054
confusion_matrix and normalize_confusion_matrix have been moved to evalml.utils #1038
confusion_matrix
normalize_confusion_matrix
All graph utility methods previously under evalml.pipelines.graph_utils have been moved to evalml.utils.graph_utils #1060
evalml.pipelines.graph_utils
evalml.utils.graph_utils
Add save/load method to components #1023
Expose pickle protocol as optional arg to save/load #1023
Updated estimators used in AutoML to include ExtraTrees and ElasticNet estimators #1030
Removed DeprecationWarning for SimpleImputer #1018
Add note about version numbers to release process docs #1034
Test files are now included in the evalml package #1029
Added string and categorical targets support for binary and multiclass pipelines and check for numeric targets for DetectLabelLeakage data check #932
Added clear exception for regression pipelines if target datatype is string or categorical #960
Added target column names and class labels in predict and predict_proba output for pipelines #951
Added _compute_shap_values and normalize_values to pipelines/explanations module #958
Added explain_prediction feature which explains single predictions with SHAP #974
Added Imputer to allow different imputation strategies for numerical and categorical dtypes #991
Added support for configuring logfile path using env var, and don’t create logger if there are filesystem errors #975
Updated catboost estimators’ default parameters and automl hyperparameter ranges to speed up fit time #998
Fixed ReadtheDocs warning failure regarding embedded gif #943
Removed incorrect parameter passed to pipeline classes in _add_baseline_pipelines #941
Added universal error for calling predict, predict_proba, transform, and feature_importances before fitting #969, #994
Made TextFeaturizer component and pip dependencies featuretools and nlp_primitives optional #976
Updated imputation strategy in automl to no longer limit impute strategy to most_frequent for all features if there are any categorical columns #991
Fixed UnboundLocalError for`cv_pipeline` when automl search errors #996
Fixed Imputer to reset dataframe index to preserve behavior expected from SimpleImputer #1009
Moved get_estimators ` to `evalml.pipelines.components.utils #934
Modified Pipelines to raise PipelineScoreError when they encounter an error during scoring #936
Moved evalml.model_families.list_model_families to evalml.pipelines.components.allowed_model_families #959
Renamed DateTimeFeaturization to DateTimeFeaturizer #977
Added check to stop search and raise an error if all pipelines in a batch return NaN scores #1015
Update README.md #963
Reworded message when errors are returned from data checks in search #982
Added section on understanding model predictions with explain_prediction to User Guide #981
Added a section to the user guide and api reference about how XGBoost and CatBoost are not fully supported. #992
Added custom components section in user guide #993
Update FAQ section formatting #997
Update release process documentation #1003
Moved predict_proba and predict tests regarding string / categorical targets to test_pipelines.py #972
Fix dependency update bot by updating python version to 3.7 to avoid frequent github version updates #1002
get_estimators has been moved to evalml.pipelines.components.utils (previously was under evalml.pipelines.utils) #934
get_estimators
evalml.pipelines.components.utils
evalml.pipelines.utils
Removed the raise_errors flag in AutoML search. All errors during pipeline evaluation will be caught and logged. #936
raise_errors
evalml.model_families.list_model_families has been moved to evalml.pipelines.components.allowed_model_families #959
evalml.model_families.list_model_families
TextFeaturizer: the featuretools and nlp_primitives packages must be installed after installing evalml in order to use this component #976
TextFeaturizer
featuretools
nlp_primitives
DateTimeFeaturization
DateTimeFeaturizer
Added NoVarianceDataCheck to DefaultDataChecks #893
Added text processing and featurization component TextFeaturizer #913, #924
Added additional checks to InvalidTargetDataCheck to handle invalid target data types #929
AutoMLSearch will now handle KeyboardInterrupt and prompt user for confirmation #915
Makes automl results a read-only property #919
Deleted static pipelines and refactored tests involving static pipelines, removed all_pipelines() and get_pipelines() #904
Moved list_model_families to evalml.model_family.utils #903
Updated all_pipelines, all_estimators, all_components to use the same mechanism for dynamically generating their elements #898
Rename master branch to main #918
Add pypi release github action #923
Updated AutoMLSearch.search stdout output and logging and removed tqdm progress bar #921
Moved automl config checks previously in search() to init #933
Reorganized and rewrote documentation #937
Updated to use pydata sphinx theme #937
Updated docs to use release_notes instead of changelog #942
Cleaned up fixture names and usages in tests #895
list_model_families has been moved to evalml.model_family.utils (previously was under evalml.pipelines.utils) #903
list_model_families
evalml.model_family.utils
Static pipeline definitions have been removed, but similar pipelines can still be constructed via creating an instance of PipelineBase #904
all_pipelines() and get_pipelines() utility methods have been removed #904
all_pipelines()
get_pipelines()
Added multiclass support for ROC curve graphing #832
Added preprocessing component to drop features whose percentage of NaN values exceeds a specified threshold #834
Added data check to check for problematic target labels #814
Added PerColumnImputer that allows imputation strategies per column #824
Added transformer to drop specific columns #827
Added support for categories, handle_error, and drop parameters in OneHotEncoder #830 #897
Added preprocessing component to handle DateTime columns featurization #838
Added ability to clone pipelines and components #842
Define getter method for component parameters #847
Added utility methods to calculate and graph permutation importances #860, #880
Added new utility functions necessary for generating dynamic preprocessing pipelines #852
Added kwargs to all components #863
Updated AutoSearchBase to use dynamically generated preprocessing pipelines #870
Added SelectColumns transformer #873
Added ability to evaluate additional pipelines for automl search #874
Added default_parameters class property to components and pipelines #879
Added better support for disabling data checks in automl search #892
Added ability to save and load AutoML objects to file #888
Updated AutoSearchBase.get_pipelines to return an untrained pipeline instance #876
Saved learned binary classification thresholds in automl results cv data dict #876
Fixed bug where SimpleImputer cannot handle dropped columns #846
Fixed bug where PerColumnImputer cannot handle dropped columns #855
Enforce requirement that builtin components save all inputted values in their parameters dict #847
Don’t list base classes in all_components output #847
Standardize all components to output pandas data structures, and accept either pandas or numpy #853
Fixed rankings and full_rankings error when search has not been run #894
Update all_pipelines and all_components to try initializing pipelines/components, and on failure exclude them #849
Refactor handle_components to handle_components_class, standardize to ComponentBase subclass instead of instance #850
Refactor “blacklist”/”whitelist” to “allow”/”exclude” lists #854
Replaced AutoClassificationSearch and AutoRegressionSearch with AutoMLSearch #871
Renamed feature_importances and permutation_importances methods to use singular names (feature_importance and permutation_importance) #883
Updated automl default data splitter to train/validation split for large datasets #877
Added open source license, update some repo metadata #887
Removed dead code in _get_preprocessing_components #896
Fix some typos and update the EvalML logo #872
Update the changelog check job to expect the new branching pattern for the deps update bot #836
Check that all components output pandas datastructures, and can accept either pandas or numpy #853
Pipelines’ static component_graph field must contain either ComponentBase subclasses or str, instead of ComponentBase subclass instances #850
component_graph
ComponentBase
str
Rename handle_component to handle_component_class. Now standardizes to ComponentBase subclasses instead of ComponentBase subclass instances #850
handle_component
handle_component_class
Renamed automl’s cv argument to data_split #877
cv
data_split
Pipelines’ and classifiers’ feature_importances is renamed feature_importance, graph_feature_importances is renamed graph_feature_importance #883
feature_importances
Passing data_checks=None to automl search will not perform any data checks as opposed to default checks. #892
data_checks=None
Pipelines to search for in AutoML are now determined automatically, rather than using the statically-defined pipeline classes. #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 #876
AutoSearchBase.get_pipelines
Added baseline models for classification and regression, add functionality to calculate baseline models before searching in AutoML #746
Port over highly-null guardrail as a data check and define DefaultDataChecks and DisableDataChecks classes #745
Update Tuner classes to work directly with pipeline parameters dicts instead of flat parameter lists #779
Add Elastic Net as a pipeline option #812
Added new Pipeline option ExtraTrees #790
Added precicion-recall curve metrics and plot for binary classification problems in evalml.pipeline.graph_utils #794
Update the default automl algorithm to search in batches, starting with default parameters for each pipeline and iterating from there #793
Added AutoMLAlgorithm class and IterativeAlgorithm impl, separated from AutoSearchBase #793
Update pipeline score to return nan score for any objective which throws an exception during scoring #787
Fixed bug introduced in #787 where binary classification metrics requiring predicted probabilities error in scoring #798
CatBoost and XGBoost classifiers and regressors can no longer have a learning rate of 0 #795
Cleanup pipeline score code, and cleanup codecov #711
Remove pass for abstract methods for codecov #730
Added __str__ for AutoSearch object #675
Add util methods to graph ROC and confusion matrix #720
Refactor AutoBase to AutoSearchBase #758
Updated AutoBase with data_checks parameter, removed previous detect_label_leakage parameter, and added functionality to run data checks before search in AutoML #765
Updated our logger to use Python’s logging utils #763
Refactor most of AutoSearchBase._do_iteration impl into AutoSearchBase._evaluate #762
Port over all guardrails to use the new DataCheck API #789
Expanded import_or_raise to catch all exceptions #759
Adds RMSE, MSLE, RMSLE as standard metrics #788
Don’t allow Recall to be used as an objective for AutoML #784
Removed feature selection from pipelines #819
Update default estimator parameters to make automl search faster and more accurate #793
Add instructions to freeze master on release.md #726
Update release instructions with more details #727 #733
Add objective base classes to API reference #736
Fix components API to match other modules #747
Delete codecov yml, use codecov.io’s default #732
Added unit tests for fraud cost, lead scoring, and standard metric objectives #741
Update codecov client #782
Updated AutoBase __str__ test to include no parameters case #783
Added unit tests for ExtraTrees pipeline #790
If codecov fails to upload, fail build #810
Updated Python version of dependency action #816
Update the dependency update bot to use a suffix when creating branches #817
The detect_label_leakage parameter for AutoML classes has been removed and replaced by a data_checks parameter #765
detect_label_leakage
data_checks
Moved ROC and confusion matrix methods from evalml.pipeline.plot_utils to evalml.pipeline.graph_utils #720
evalml.pipeline.plot_utils
evalml.pipeline.graph_utils
Tuner classes require a pipeline hyperparameter range dict as an init arg instead of a space definition #779
Tuner
Tuner.propose and Tuner.add work directly with pipeline parameters dicts instead of flat parameter lists #779
Tuner.propose
Tuner.add
PipelineBase.hyperparameters and custom_hyperparameters use pipeline parameters dict format instead of being represented as a flat list #779
PipelineBase.hyperparameters
custom_hyperparameters
All guardrail functions previously under evalml.guardrails.utils will be removed and replaced by data checks #789
evalml.guardrails.utils
Recall disallowed as an objective for AutoML #784
AutoSearchBase parameter tuner has been renamed to tuner_class #793
AutoSearchBase
tuner
tuner_class
AutoSearchBase parameter possible_pipelines and possible_model_families have been renamed to allowed_pipelines and allowed_model_families #793
possible_pipelines
possible_model_families
allowed_pipelines
allowed_model_families
Added accuracy as an standard objective #624
Added verbose parameter to load_fraud #560
Added Balanced Accuracy metric for binary, multiclass #612 #661
Added XGBoost regressor and XGBoost regression pipeline #666
Added Accuracy metric for multiclass #672
Added objective name in AutoBase.describe_pipeline #686
Added DataCheck and DataChecks, Message classes and relevant subclasses #739
Removed direct access to cls.component_graph #595
Add testing files to .gitignore #625
Remove circular dependencies from Makefile #637
Add error case for normalize_confusion_matrix() #640
Fixed XGBoostClassifier and XGBoostRegressor bug with feature names that contain [, ], or < #659
Update make_pipeline_graph to not accidentally create empty file when testing if path is valid #649
Fix pip installation warning about docsutils version, from boto dependency #664
Removed zero division warning for F1/precision/recall metrics #671
Fixed summary for pipelines without estimators #707
Updated default objective for binary/multiseries classification to log loss #613
Created classification and regression pipeline subclasses and removed objective as an attribute of pipeline classes #405
Changed the output of score to return one dictionary #429
Created binary and multiclass objective subclasses #504
Updated objectives API #445
Removed call to get_plot_data from AutoML #615
Set raise_error to default to True for AutoML classes #638
Remove unnecessary “u” prefixes on some unicode strings #641
Changed one-hot encoder to return uint8 dtypes instead of ints #653
Pipeline _name field changed to custom_name #650
Removed graphs.py and moved methods into PipelineBase #657, #665
Remove s3fs as a dev dependency #664
Changed requirements-parser to be a core dependency #673
Replace supported_problem_types field on pipelines with problem_type attribute on base classes #678
Changed AutoML to only show best results for a given pipeline template in rankings, added full_rankings property to show all #682
Update ModelFamily values: don’t list xgboost/catboost as classifiers now that we have regression pipelines for them #677
Changed AutoML’s describe_pipeline to get problem type from pipeline instead #685
Standardize import_or_raise error messages #683
Updated argument order of objectives to align with sklearn’s #698
Renamed pipeline.feature_importance_graph to pipeline.graph_feature_importances #700
Moved ROC and confusion matrix methods to evalml.pipelines.plot_utils #704
Renamed MultiClassificationObjective to MulticlassClassificationObjective, to align with pipeline naming scheme #715
Fixed some sphinx warnings #593
Fixed docstring for AutoClassificationSearch with correct command #599
Limit readthedocs formats to pdf, not htmlzip and epub #594 #600
Clean up objectives API documentation #605
Fixed function on Exploring search results page #604
Update release process doc #567
AutoClassificationSearch and AutoRegressionSearch show inherited methods in API reference #651
Fixed improperly formatted code in breaking changes for changelog #655
Added configuration to treat Sphinx warnings as errors #660
Removed separate plotting section for pipelines in API reference #657, #665
Have leads example notebook load S3 files using https, so we can delete s3fs dev dependency #664
Categorized components in API reference and added descriptions for each category #663
Fixed Sphinx warnings about BalancedAccuracy objective #669
Updated API reference to include missing components and clean up pipeline docstrings #689
Reorganize API ref, and clarify pipeline sub-titles #688
Add and update preprocessing utils in API reference #687
Added inheritance diagrams to API reference #695
Documented which default objective AutoML optimizes for #699
Create seperate install page #701
Include more utils in API ref, like import_or_raise #704
Add more color to pipeline documentation #705
Matched install commands of check_latest_dependencies test and it’s GitHub action #578
Added Github app to auto assign PR author as assignee #477
Removed unneeded conda installation of xgboost in windows checkin tests #618
Update graph tests to always use tmpfile dir #649
Changelog checkin test workaround for release PRs: If ‘future release’ section is empty of PR refs, pass check #658
Add changelog checkin test exception for dep-update branch #723
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.
fit()
predict()
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()
objectives
score() will now return one dictionary of all objective scores.
ROC and ConfusionMatrix plot methods via Auto(*).plot have been removed by #615 and are replaced by roc_curve and confusion_matrix in evamlm.pipelines.plot_utils` in #704
ROC
ConfusionMatrix
Auto(*).plot
roc_curve
normalize_confusion_matrix has been moved to evalml.pipelines.plot_utils #704
evalml.pipelines.plot_utils
Pipelines _name field changed to custom_name
_name
custom_name
Pipelines supported_problem_types field is removed because it is no longer necessary #678
supported_problem_types
Updated argument order of objectives’ objective_function to align with sklearn #698
pipeline.feature_importance_graph has been renamed to pipeline.graph_feature_importances in #700
Removed unsupported MSLE objective #704
MSLE
Add normalization option and information to confusion matrix #484
Add util function to drop rows with NaN values #487
Renamed PipelineBase.name as PipelineBase.summary and redefined PipelineBase.name as class property #491
Added access to parameters in Pipelines with PipelineBase.parameters (used to be return of PipelineBase.describe) #501
Added fill_value parameter for SimpleImputer #509
Added functionality to override component hyperparameters and made pipelines take hyperparemeters from components #516
Allow numpy.random.RandomState for random_state parameters #556
Removed unused dependency matplotlib, and move category_encoders to test reqs #572
Undo version cap in XGBoost placed in #402 and allowed all released of XGBoost #407
Support pandas 1.0.0 #486
Made all references to the logger static #503
Refactored model_type parameter for components and pipelines to model_family #507
Refactored problem_types for pipelines and components into supported_problem_types #515
Moved pipelines/utils.save_pipeline and pipelines/utils.load_pipeline to PipelineBase.save and PipelineBase.load #526
Limit number of categories encoded by OneHotEncoder #517
Updated API reference to remove PipelinePlot and added moved PipelineBase plotting methods #483
Add code style and github issue guides #463 #512
Updated API reference for to surface class variables for pipelines and components #537
Fixed README documentation link #535
Unhid PR references in changelog #656
Added automated dependency check PR #482, #505
Updated automated dependency check comment #497
Have build_docs job use python executor, so that env vars are set properly #547
Added simple test to make sure OneHotEncoder’s top_n works with large number of categories #552
Run windows unit tests on PRs #557
AutoClassificationSearch and AutoRegressionSearch’s model_types parameter has been refactored into allowed_model_families
AutoClassificationSearch
AutoRegressionSearch
model_types
ModelTypes enum has been changed to ModelFamily
ModelTypes
ModelFamily
Components and Pipelines now have a model_family field instead of model_type
model_family
model_type
get_pipelines utility function now accepts model_families as an argument instead of model_types
get_pipelines
model_families
PipelineBase.name no longer returns structure of pipeline and has been replaced by PipelineBase.summary
PipelineBase.name
PipelineBase.summary
PipelineBase.problem_types and Estimator.problem_types has been renamed to supported_problem_types
PipelineBase.problem_types
Estimator.problem_types
pipelines/utils.save_pipeline and pipelines/utils.load_pipeline moved to PipelineBase.save and PipelineBase.load
pipelines/utils.save_pipeline
pipelines/utils.load_pipeline
PipelineBase.save
PipelineBase.load
Added emacs buffers to .gitignore #350
Add CatBoost (gradient-boosted trees) classification and regression components and pipelines #247
Added Tuner abstract base class #351
Added n_jobs as parameter for AutoClassificationSearch and AutoRegressionSearch #403
Changed colors of confusion matrix to shades of blue and updated axis order to match scikit-learn’s #426
Added PipelineBase graph and feature_importance_graph methods, moved from previous location #423
Added support for python 3.8 #462
Fixed ROC and confusion matrix plots not being calculated if user passed own additional_objectives #276
Fixed ReadtheDocs FileNotFoundError exception for fraud dataset #439
Added n_estimators as a tunable parameter for XGBoost #307
Remove unused parameter ObjectiveBase.fit_needs_proba #320
Remove extraneous parameter component_type from all components #361
Remove unused rankings.csv file #397
Downloaded demo and test datasets so unit tests can run offline #408
Remove _needs_fitting attribute from Components #398
Changed plot.feature_importance to show only non-zero feature importances by default, added optional parameter to show all #413
Refactored PipelineBase to take in parameter dictionary and moved pipeline metadata to class attribute #421
Dropped support for Python 3.5 #438
Removed unused apply.py file #449
Clean up requirements.txt to remove unused deps #451
Support installation without all required dependencies #459
Update release.md with instructions to release to internal license key #354
Added tests for utils (and moved current utils to gen_utils) #297
Moved XGBoost install into it’s own separate step on Windows using Conda #313
Rewind pandas version to before 1.0.0, to diagnose test failures for that version #325
Added dependency update checkin test #324
Rewind XGBoost version to before 1.0.0 to diagnose test failures for that version #402
Update dependency check to use a whitelist #417
Update unit test jobs to not install dev deps #455
Python 3.5 will not be actively supported.
Added ability to create a plot of feature importances #133
Add early stopping to AutoML using patience and tolerance parameters #241
Added ROC and confusion matrix metrics and plot for classification problems and introduce PipelineSearchPlots class #242
Enhanced AutoML results with search order #260
Added utility function to show system and environment information #300
Lower botocore requirement #235
Fixed decision_function calculation for FraudCost objective #254
Fixed return value of Recall metrics #264
Components return self on fit #289
Renamed automl classes to AutoRegressionSearch and AutoClassificationSearch #287
Updating demo datasets to retain column names #223
Moving pipeline visualization to PipelinePlots class #228
Standarizing inputs as pd.Dataframe / pd.Series #130
Enforcing that pipelines must have an estimator as last component #277
Added ipywidgets as a dependency in requirements.txt #278
Added Random and Grid Search Tuners #240
Adding class properties to API reference #244
Fix and filter FutureWarnings from scikit-learn #249, #257
Adding Linear Regression to API reference and cleaning up some Sphinx warnings #227
Added support for testing on Windows with CircleCI #226
Added support for doctests #233
The fit() method for AutoClassifier and AutoRegressor has been renamed to search().
AutoClassifier
AutoRegressor
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.
AutoClassificationSearch.results
AutoRegressionSearch.results
pipeline_results
search_order
.results
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.
component_list
NotImplementedError
Adding basic pipeline structure visualization #211
Added notebooks to build process #212
Added basic outlier detection guardrail #151
Added basic ID column guardrail #135
Added support for unlimited pipelines with a max_time limit #70
Updated .readthedocs.yaml to successfully build #188
Removed MSLE from default additional objectives #203
Fixed random_state passed in pipelines #204
Fixed slow down in RFRegressor #206
Pulled information for describe_pipeline from pipeline’s new describe method #190
Refactored pipelines #108
Removed guardrails from Auto(*) #202, #208
Updated documentation to show max_time enhancements #189
Updated release instructions for RTD #193
Added contributing instructions #213
Added new content #222
Added basic one hot encoding #73
Use enums for model_type #110
Support for splitting regression datasets #112
Auto-infer multiclass classification #99
Added support for other units in max_time #125
Detect highly null columns #121
Added additional regression objectives #100
Show an interactive iteration vs. score plot when using fit() #134
Reordered describe_pipeline #94
Added type check for model_type #109
Fixed s units when setting string max_time #132
Fix objectives not appearing in API documentation #150
Reorganized tests #93
Moved logging to its own module #119
Show progress bar history #111
Using cloudpickle instead of pickle to allow unloading of custom objectives #113
Removed render.py #154
Update release instructions #140
Include additional_objectives parameter #124
Added Changelog #136
Code coverage #90
Added CircleCI tests for other Python versions #104
Added doc notebooks as tests #139
Test metadata for CircleCI and 2 core parallelism #137
Added AutoML for classification and regressor using Autobase and Skopt #7 #9
Implemented standard classification and regression metrics #7
Added logistic regression, random forest, and XGBoost pipelines #7
Implemented support for custom objectives #15
Feature importance for pipelines #18
Serialization for pipelines #19
Allow fitting on objectives for optimal threshold #27
Added detect label leakage #31
Implemented callbacks #42
Allow for multiclass classification #21
Added support for additional objectives #79
Fixed feature selection in pipelines #13
Made random_seed usage consistent #45
Added docstrings #6
Created notebooks for docs #6
Initialized readthedocs EvalML #6
Added favicon #38
Added testing for loading data #39
Created fraud detection objective #4
First Release
Added lead scoring objecitve #1
Added basic classifier #1
Initialized Sphinx for docs #1