Release Notes

Future Releases

• Enhancements

• Fixes

• Changes

• Documentation Changes

• Testing Changes

v0.16.0 Nov. 24, 2020

• Enhancements

  • Updated pipelines and make_pipeline to accept Woodwork inputs #1393

  • Updated components to accept Woodwork inputs #1423

  • Added ability to freeze hyperparameters for AutoMLSearch #1284

  • Added Target Encoder into transformer components #1401

  • Added callback for error handling in AutoMLSearch #1403

  • Added the index id to the explain_predictions_best_worst output to help users identify which rows in their data are included #1365

  • The top_k features displayed in explain_predictions_* functions are now determined by the magnitude of shap values as opposed to the top_k largest and smallest shap values. #1374

  • Added a problem type for time series regression #1386

  • Added a is_defined_for_problem_type method to ObjectiveBase #1386

  • Added a random_state parameter to make_pipeline_from_components function #1411

  • Added DelayedFeaturesTransformer #1396

  • Added a TimeSeriesRegressionPipeline class #1418

  • Removed core-requirements.txt from the package distribution #1429

  • Updated data check messages to include a “code” and “details” fields #1451, #1462

  • Added a TimeSeriesSplit data splitter for time series problems #1441

  • Added a problem_configuration parameter to AutoMLSearch #1457

• Fixes

  • Fixed IndexError raised in AutoMLSearch when ensembling = True but only one pipeline to iterate over #1397

  • Fixed stacked ensemble input bug and LightGBM warning and bug in AutoMLSearch #1388

  • Updated enum classes to show possible enum values as attributes #1391

  • Updated calls to Woodwork’s to_pandas() to to_series() and to_dataframe() #1428

  • Fixed bug in OHE where column names were not guaranteed to be unique #1349

  • Fixed bug with percent improvement of ExpVariance objective on data with highly skewed target #1467

• Changes

  • Changed OutliersDataCheck to return the list of columns, rather than rows, that contain outliers #1377

  • Simplified and cleaned output for Code Generation #1371

  • Reverted changes from #1337 #1409

  • Updated data checks to return dictionary of warnings and errors instead of a list #1448

  • Updated AutoMLSearch to pass Woodwork data structures to every pipeline (instead of pandas DataFrames) #1450

  • Update AutoMLSearch to default to max_batches=1 instead of max_iterations=5 #1452

• Documentation Changes

  • Added description of CLA to contributing guide, updated description of draft PRs #1402

  • Updated documentation to include all data checks, DataChecks, and usage of data checks in AutoML #1412

  • Updated docstrings from np.array to np.ndarray #1417

  • Added section on stacking ensembles in AutoMLSearch documentation #1425

• Testing Changes

  • Removed category_encoders from test-requirements.txt #1373

  • Tweak codecov.io settings again to avoid flakes #1413

  • Modified make lint to check notebook versions in the docs #1431

  • Modified make lint-fix to standardize notebook versions in the docs #1431

  • Use new version of pull request Github Action for dependency check (#1443)

  • Reduced number of workers for tests to 4 #1447

Warning

Breaking Changes

• The top_k and top_k_features parameters in explain_predictions_* functions now return k features as opposed to 2 * k features #1374

• Renamed problem_type to problem_types in RegressionObjective, BinaryClassificationObjective, and MulticlassClassificationObjective #1319

• Data checks now return a dictionary of warnings and errors instead of a list #1448

v0.15.0 Oct. 29, 2020

• Enhancements

  • Added stacked ensemble component classes (StackedEnsembleClassifier, StackedEnsembleRegressor) #1134

  • Added stacked ensemble components to AutoMLSearch #1253

  • Added DecisionTreeClassifier and DecisionTreeRegressor to AutoML #1255

  • Added graph_prediction_vs_actual in model_understanding for regression problems #1252

  • Added parameter to OneHotEncoder to enable filtering for features to encode for #1249

  • Added percent-better-than-baseline for all objectives to automl.results #1244

  • Added HighVarianceCVDataCheck and replaced synonymous warning in AutoMLSearch #1254

  • Added PCA Transformer component for dimensionality reduction #1270

  • Added generate_pipeline_code and generate_component_code to allow for code generation given a pipeline or component instance #1306

  • Added PCA Transformer component for dimensionality reduction #1270

  • Updated AutoMLSearch to support Woodwork data structures #1299

  • Added cv_folds to ClassImbalanceDataCheck and added this check to DefaultDataChecks #1333

  • Make max_batches argument to AutoMLSearch.search public #1320

  • Added text support to automl search #1062

  • Added _pipelines_per_batch as a private argument to AutoMLSearch #1355

• Fixes

  • Fixed ML performance issue with ordered datasets: always shuffle data in automl’s default CV splits #1265

  • Fixed broken evalml info CLI command #1293

  • Fixed boosting type='rf' for LightGBM Classifier, as well as num_leaves error #1302

  • Fixed bug in explain_predictions_best_worst where a custom index in the target variable would cause a ValueError #1318

  • Added stacked ensemble estimators to to evalml.pipelines.__init__ file #1326

  • Fixed bug in OHE where calls to transform were not deterministic if top_n was less than the number of categories in a column #1324

  • Fixed LightGBM warning messages during AutoMLSearch #1342

  • Fix warnings thrown during AutoMLSearch in HighVarianceCVDataCheck #1346

  • Fixed bug where TrainingValidationSplit would return invalid location indices for dataframes with a custom index #1348

  • Fixed bug where the AutoMLSearch random_state was not being passed to the created pipelines #1321

• Changes

  • Allow add_to_rankings to be called before AutoMLSearch is called #1250

  • Removed Graphviz from test-requirements to add to requirements.txt #1327

  • Removed max_pipelines parameter from AutoMLSearch #1264

  • Include editable installs in all install make targets #1335

  • Made pip dependencies featuretools and nlp_primitives core dependencies #1062

  • Removed PartOfSpeechCount from TextFeaturizer transform primitives #1062

  • Added warning for partial_dependency when the feature includes null values #1352

• Documentation Changes

  • Fixed and updated code blocks in Release Notes #1243

  • Added DecisionTree estimators to API Reference #1246

  • Changed class inheritance display to flow vertically #1248

  • Updated cost-benefit tutorial to use a holdout/test set #1159

  • Added evalml info command to documentation #1293

  • Miscellaneous doc updates #1269

  • Removed conda pre-release testing from the release process document #1282

  • Updates to contributing guide #1310

  • Added Alteryx footer to docs with Twitter and Github link #1312

  • Added documentation for evalml installation for Python 3.6 #1322

  • Added documentation changes to make the API Docs easier to understand #1323

  • Fixed documentation for feature_importance #1353

  • Added tutorial for running AutoML with text data #1357

  • Added documentation for woodwork integration with automl search #1361

• Testing Changes

  • Added tests for jupyter_check to handle IPython #1256

  • Cleaned up make_pipeline tests to test for all estimators #1257

  • Added a test to check conda build after merge to main #1247

  • Removed code that was lacking codecov for __main__.py and unnecessary #1293

  • Codecov: round coverage up instead of down #1334

  • Add DockerHub credentials to CI testing environment #1356

  • Add DockerHub credentials to conda testing environment #1363

Warning

Breaking Changes

• Renamed LabelLeakageDataCheck to TargetLeakageDataCheck #1319

• max_pipelines parameter has been removed from AutoMLSearch. Please use max_iterations instead. #1264

• AutoMLSearch.search() will now log a warning if the input is not a Woodwork data structure (pandas, numpy) #1299

• Make max_batches argument to AutoMLSearch.search public #1320

• Removed unused argument feature_types from AutoMLSearch.search #1062

v0.14.1 Sep. 29, 2020

• Enhancements

  • Updated partial dependence methods to support calculating numeric columns in a dataset with non-numeric columns #1150

  • Added get_feature_names on OneHotEncoder #1193

  • Added detect_problem_type to problem_type/utils.py to automatically detect the problem type given targets #1194

  • Added LightGBM to AutoMLSearch #1199

  • Updated scikit-learn and scikit-optimize to use latest versions - 0.23.2 and 0.8.1 respectively #1141

  • Added __str__ and __repr__ for pipelines and components #1218

  • Included internal target check for both training and validation data in AutoMLSearch #1226

  • Added ProblemTypes.all_problem_types helper to get list of supported problem types #1219

  • Added DecisionTreeClassifier and DecisionTreeRegressor classes #1223

  • Added ProblemTypes.all_problem_types helper to get list of supported problem types #1219

  • DataChecks can now be parametrized by passing a list of DataCheck classes and a parameter dictionary #1167

  • Added first CV fold score as validation score in AutoMLSearch.rankings #1221

  • Updated flake8 configuration to enable linting on __init__.py files #1234

  • Refined make_pipeline_from_components implementation #1204

• Fixes

  • Updated GitHub URL after migration to Alteryx GitHub org #1207

  • Changed Problem Type enum to be more similar to the string name #1208

  • Wrapped call to scikit-learn’s partial dependence method in a try/finally block #1232

• Changes

  • Added allow_writing_files as a named argument to CatBoost estimators. #1202

  • Added solver and multi_class as named arguments to LogisticRegressionClassifier #1202

  • Replaced pipeline’s ._transform method to evaluate all the preprocessing steps of a pipeline with .compute_estimator_features #1231

  • Changed default large dataset train/test splitting behavior #1205

• Documentation Changes

  • Included description of how to access the component instances and features for pipeline user guide #1163

  • Updated API docs to refer to target as “target” instead of “labels” for non-classification tasks and minor docs cleanup #1160

  • Added Class Imbalance Data Check to api_reference.rst #1190 #1200

  • Added pipeline properties to API reference #1209

  • Clarified what the objective parameter in AutoML is used for in AutoML API reference and AutoML user guide #1222

  • Updated API docs to include skopt.space.Categorical option for component hyperparameter range definition #1228

  • Added install documentation for libomp in order to use LightGBM on Mac #1233

  • Improved description of max_iterations in documentation #1212

  • Removed unused code from sphinx conf #1235

• Testing Changes

Warning

Breaking Changes

• DefaultDataChecks now accepts a problem_type parameter that must be specified #1167

• Pipeline’s ._transform method to evaluate all the preprocessing steps of a pipeline has been replaced with .compute_estimator_features #1231

• get_objectives has been renamed to get_core_objectives. This function will now return a list of valid objective instances #1230

v0.13.2 Sep. 17, 2020

• Enhancements

  • 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

• Fixes

  • 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

• Changes

  • 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

  • Rename max_pipelines to max_iterations #1169

• Documentation Changes

  • 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

• Testing Changes

  • 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

Breaking Changes

• 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

• Passing “Cost Benefit Matrix”, “Fraud Cost”, “Lead Scoring”, “Mean Squared Log Error”,

  “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

• max_pipelines now raises a DeprecationWarning and will be removed in the next release. max_iterations should be used instead. #1169

v0.13.1 Aug. 25, 2020

• Enhancements

  • 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

• Fixes

  • 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

• Changes

  • 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

• Documentation Changes

  • 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

• Testing Changes

  • Refactor CircleCI tests to use matrix jobs (#1043)

  • Added a test to check that all test directories are included in evalml package #1054

Warning

Breaking Changes

• confusion_matrix and normalize_confusion_matrix have been moved to evalml.utils #1038

• All graph utility methods previously under evalml.pipelines.graph_utils have been moved to evalml.utils.graph_utils #1060

v0.12.2 Aug. 6, 2020

• Enhancements

  • 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

• Fixes

• Changes

  • Removed DeprecationWarning for SimpleImputer #1018

• Documentation Changes

  • Add note about version numbers to release process docs #1034

• Testing Changes

  • Test files are now included in the evalml package #1029

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 #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

• Fixes

  • 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

• Changes

  • 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

• Documentation Changes

  • Updated 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

  • Updated FAQ section formatting #997

  • Updated release process documentation #1003

• Testing Changes

  • Moved predict_proba and predict tests regarding string / categorical targets to test_pipelines.py #972

  • Fixed dependency update bot by updating python version to 3.7 to avoid frequent github version updates #1002

Warning

Breaking Changes

• get_estimators has been moved to evalml.pipelines.components.utils (previously was under evalml.pipelines.utils) #934

• Removed the raise_errors flag in AutoML search. All errors during pipeline evaluation will be caught and logged. #936

• evalml.model_families.list_model_families has been moved to evalml.pipelines.components.allowed_model_families #959

• TextFeaturizer: the featuretools and nlp_primitives packages must be installed after installing evalml in order to use this component #976

• Renamed DateTimeFeaturization to DateTimeFeaturizer #977

v0.11.2 July 16, 2020

• Enhancements

  • 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

• Fixes

  • Makes automl results a read-only property #919

• Changes

  • 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

• Documentation Changes

  • Reorganized and rewrote documentation #937

  • Updated to use pydata sphinx theme #937

  • Updated docs to use release_notes instead of changelog #942

• Testing Changes

  • Cleaned up fixture names and usages in tests #895

Warning

Breaking Changes

• list_model_families has been moved to evalml.model_family.utils (previously was under evalml.pipelines.utils) #903

• get_estimators has been moved to evalml.pipelines.components.utils (previously was under evalml.pipelines.utils) #934

• 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

v0.11.0 June 30, 2020

• Enhancements

  • 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

• Fixes

  • 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

• Changes

  • 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

• Documentation Changes

  • Fix some typos and update the EvalML logo #872

• Testing Changes

  • 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

  • Replaced AutoClassificationSearch and AutoRegressionSearch with AutoMLSearch #871

Warning

Breaking Changes

• Pipelines’ static component_graph field must contain either ComponentBase subclasses or str, instead of ComponentBase subclass instances #850

• Rename handle_component to handle_component_class. Now standardizes to ComponentBase subclasses instead of ComponentBase subclass instances #850

• Renamed automl’s cv argument to data_split #877

• Pipelines’ and classifiers’ feature_importances is renamed feature_importance, graph_feature_importances is renamed graph_feature_importance #883

• Passing data_checks=None to automl search will not perform any data checks as opposed to default checks. #892

• 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

v0.10.0 May 29, 2020

• Enhancements

  • 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

• Fixes

  • 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

• Changes

  • 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

• Documentation Changes

  • 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

• Testing Changes

  • 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

Warning

Breaking Changes

• The detect_label_leakage parameter for AutoML classes has been removed and replaced by a data_checks parameter #765

• Moved ROC and confusion matrix methods from evalml.pipeline.plot_utils to evalml.pipeline.graph_utils #720

• Tuner classes require a pipeline hyperparameter range dict as an init arg instead of a space definition #779

• Tuner.propose and Tuner.add work directly with pipeline parameters dicts instead of flat parameter lists #779

• PipelineBase.hyperparameters and custom_hyperparameters use pipeline parameters dict format instead of being represented as a flat list #779

• All guardrail functions previously under evalml.guardrails.utils will be removed and replaced by data checks #789

• Recall disallowed as an objective for AutoML #784

• AutoSearchBase parameter tuner has been renamed to tuner_class #793

• AutoSearchBase parameter possible_pipelines and possible_model_families have been renamed to allowed_pipelines and allowed_model_families #793

v0.9.0 Apr. 27, 2020

• Enhancements

  • 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

• Fixes

  • 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

• Changes

  • Updated default objective for binary/multiclass 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

• Documentation Changes

  • 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

• Testing Changes

  • 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

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 #615 and are replaced by roc_curve and confusion_matrix in evamlm.pipelines.plot_utils in #704

• normalize_confusion_matrix has been moved to evalml.pipelines.plot_utils #704

• Pipelines _name field changed to custom_name

• Pipelines supported_problem_types field is removed because it is no longer necessary #678

• 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

v0.8.0 Apr. 1, 2020

• Enhancements

  • 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

• Fixes

  • Removed unused dependency matplotlib, and move category_encoders to test reqs #572

• Changes

  • 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

• Documentation Changes

  • 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

• Testing Changes

  • 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

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 #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

• Fixes

  • Fixed ROC and confusion matrix plots not being calculated if user passed own additional_objectives #276

  • Fixed ReadtheDocs FileNotFoundError exception for fraud dataset #439

• Changes

  • 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

• Documentation Changes

  • Update release.md with instructions to release to internal license key #354

• Testing Changes

  • 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

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 #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

• Fixes

  • 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

• Changes

  • Renamed automl classes to AutoRegressionSearch and AutoClassificationSearch #287

  • Updating demo datasets to retain column names #223

  • Moving pipeline visualization to PipelinePlot 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

• Documentation Changes

  • 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

• Testing Changes

  • Added support for testing on Windows with CircleCI #226

  • Added support for doctests #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 #211

• Documentation Changes

  • Added notebooks to build process #212

v0.5.1 Nov. 15, 2019

• Enhancements

  • 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

• Fixes

  • Removed MSLE from default additional objectives #203

  • Fixed random_state passed in pipelines #204

  • Fixed slow down in RFRegressor #206

• Changes

  • Pulled information for describe_pipeline from pipeline’s new describe method #190

  • Refactored pipelines #108

  • Removed guardrails from Auto(*) #202, #208

• Documentation Changes

  • Updated documentation to show max_time enhancements #189

  • Updated release instructions for RTD #193

  • Added notebooks to build process #212

  • Added contributing instructions #213

  • Added new content #222

v0.5.0 Oct. 29, 2019

• Enhancements

  • 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

• Fixes

  • 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

• Changes

  • 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

• Documentation Changes

  • Update release instructions #140

  • Include additional_objectives parameter #124

  • Added Changelog #136

• Testing Changes

  • 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

v0.4.1 Sep. 16, 2019

• Enhancements

  • 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

• Fixes

  • Fixed feature selection in pipelines #13

  • Made random_seed usage consistent #45

• Documentation Changes

  • Documentation Changes

  • Added docstrings #6

  • Created notebooks for docs #6

  • Initialized readthedocs EvalML #6

  • Added favicon #38

• Testing Changes

  • Added testing for loading data #39

v0.2.0 Aug. 13, 2019

• Enhancements

  • Created fraud detection objective #4

v0.1.0 July. 31, 2019

• First Release

• Enhancements

  • Added lead scoring objecitve #1

  • Added basic classifier #1

• Documentation Changes

  • Initialized Sphinx for docs #1

evalml.utils.drop_rows_with_nans