Release v0.15.0

[dsherry]

v0.15.0 Oct. 29, 2020

Enhancements

• Added stacked ensemble component classes (StackedEnsembleClassifier, StackedEnsembleRegressor) Implement stacked ensemble classes #1134
• Added stacked ensemble components to AutoMLSearch Integrate ensemble methods in AutoML #1253
• Added DecisionTreeClassifier and DecisionTreeRegressor to AutoML Add DecisionTreeClassifier and DecisionTreeRegressor to AutoML #1255
• Added graph_prediction_vs_actual in model_understanding for regression problems Add Plot for Prediction vs Actual for Regression Problems #1252
• Added parameter to OneHotEncoder to enable filtering for features to encode for Allow user to filter which features to encode for OneHotEncoder #1249
• Added percent-better-than-baseline for all objectives to automl.results Compute percent-better-than-baseline for all objectives #1244
• Added HighVarianceCVDataCheck and replaced synonymous warning in AutoMLSearch Add HighVarianceCVDataCheck #1254
• Added PCA Transformer component for dimensionality reduction Add PCA component #1270
• Added generate_pipeline_code and generate_component_code to allow for code generation given a pipeline or component instance Code Generation for Pipelines and Components #1306
• Added PCA Transformer component for dimensionality reduction Add PCA component #1270
• Updated AutoMLSearch to support Woodwork data structures Update AutoMLSearch to support WoodWork DataTables #1299
• Added cv_folds to ClassImbalanceDataCheck and added this check to DefaultDataChecks Add ClassImbalanceDataCheck to DefaultDataChecks #1333
• Make max_batches argument to AutoMLSearch.search public Added max_batches as a public parameter #1320
• Added text support to automl search Integrate TextFeaturizer to automl #1062
• Added _pipelines_per_batch as a private argument to AutoMLSearch 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 Always shuffle data in default automl data split strategies #1265
• Fixed broken evalml info CLI command Clean up lacking codecov for __main__.py and add evalml info to docs #1293
• Fixed boosting type='rf' for LightGBM Classifier, as well as num_leaves error Fix 'RF' error for LightGBM Classifier #1302
• Fixed bug in explain_predictions_best_worst where a custom index in the target variable would cause a ValueError explain_predictions_best_worst custom index bug fix #1318
• Added stacked ensemble estimators to to evalml.pipelines.__init__ file Add stacked ensemble to __init__ file for pipelines #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 Make OHE deterministic when top_n < no. of categories #1324
• Fixed LightGBM warning messages during AutoMLSearch Hide LightGBM warnings #1342
• Fix warnings thrown during AutoMLSearch in HighVarianceCVDataCheck Fix 'Invalid Value Encountered' error during AutomLSearch #1346
• Fixed bug where TrainingValidationSplit would return invalid location indices for dataframes with a custom index Fix bug with TrainingValidationSplit and custom index #1348
• Fixed bug where the AutoMLSearch random_state was not being passed to the created pipelines Passing random state to pipelines created by IterativeAlgorithm next_batch #1321

Changes

• Allow add_to_rankings to be called before AutoMLSearch is called allow add_to_rankings to work before search #1250
• Removed Graphviz from test-requirements to add to requirements.txt Move Graphviz to Requirements.txt #1327
• Removed max_pipelines parameter from AutoMLSearch Remove max_pipelines parameter from AutoMLSearch #1264
• Include editable installs in all install make targets Include editable installs in all install make targets #1335
• Made pip dependencies featuretools and nlp_primitives core dependencies Integrate TextFeaturizer to automl #1062
• Removed PartOfSpeechCount from TextFeaturizer transform primitives Integrate TextFeaturizer to automl #1062
• Added warning for partial_dependency when the feature includes null values Warning on null values for partial_dependency #1352

Documentation Changes

• Fixed and updated code blocks in Release Notes Fix code in release notes #1243
• Added DecisionTree estimators to API Reference Add DecisionTree estimators to API Reference #1246
• Changed class inheritance display to flow vertically update inheritance diagrams #1248
• Updated cost-benefit tutorial to use a holdout/test set Update cost-benefit tutorial to use a holdout/test set #1159
• Added evalml info command to documentation Clean up lacking codecov for __main__.py and add evalml info to docs #1293
• Miscellaneous doc updates Changed copyright date #1269
• Removed conda pre-release testing from the release process document Removing conda pre-release check from the release process documents. #1282
• Updates to contributing guide Updates to contributing.md after working with Raymond to set up dev env #1310
• Added Alteryx footer to docs with Twitter and Github link Add Footer to Documentation #1312
• Added documentation for evalml installation for Python 3.6 Documentation: fix install error for Python 3.6 #1322
• Added documentation changes to make the API Docs easier to understand Minor Documentation Changes for the API #1323
• Fixed documentation for feature_importance Fix docstring for feature_importance #1353
• Added tutorial for running AutoML with text data Add a tutorial for text data #1357
• Added documentation for woodwork integration with automl search Update evalml docs to mention woodwork #1361

Testing Changes

• Added tests for jupyter_check to handle IPython Add Test Coverage for IPython #1256
• Cleaned up make_pipeline tests to test for all estimators Clean up make_pipeline tests #1257
• Added a test to check conda build after merge to main Build conda on merge to main #1247
• Removed code that was lacking codecov for __main__.py and unnecessary Clean up lacking codecov for __main__.py and add evalml info to docs #1293
• Codecov: round coverage up instead of down Add codecov yaml, round coverage up instead of down #1334
• Add DockerHub credentials to CI testing environment Add DockerHub credentials to CI testing environment #1356
• Add DockerHub credentials to conda testing environment Fix conda build: use the correct DockerHub credentials #1363

Breaking Changes

• Renamed LabelLeakageDataCheck to TargetLeakageDataCheck Rename LabelLeakageDataCheck to TargetLeakageDataCheck. #1319
• max_pipelines parameter has been removed from AutoMLSearch. Please use max_iterations instead. Remove max_pipelines parameter from AutoMLSearch #1264
• AutoMLSearch.search() will now log a warning if the input is not a Woodwork data structure (pandas, numpy) Update AutoMLSearch to support WoodWork DataTables #1299
• Make max_batches argument to AutoMLSearch.search public Added max_batches as a public parameter #1320
• Removed unused argument feature_types from AutoMLSearch.search Integrate TextFeaturizer to automl #1062

[codecov]

Codecov Report

Merging #1370 into main will not change coverage.
The diff coverage is 100.00%.

@@           Coverage Diff           @@
##             main    #1370   +/-   ##
=======================================
  Coverage   99.95%   99.95%           
=======================================
  Files         213      213           
  Lines       13857    13857           
=======================================
  Hits        13850    13850           
  Misses          7        7           

Impacted Files	Coverage Δ
evalml/__init__.py	100.00% <100.00%> (ø)

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[bchen1116]

🚢

[freddyaboulton]

How do the perf tests look?

[dsherry]

Perf test results here