Evalml

**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 hyperparemeters and made pipelines take hyperparemeters from components 516
- Allow numpy.random.RandomState for random_state parameters 556

**Fixes**

**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
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
- Run windows unit tests on PRs 557


**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- `PipelineBase.name` no longer returns structure of pipeline and has been replaced by `PipelineBase.summary`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`

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
- Dropped support for Python 3.5 438
- Removed unused `apply.py` file 449
- Clean up requirements.txt to remove unused deps 451


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

Breaking Changes
- Python 3.5 will not be actively supported.

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

Fixes
- Lower botocore requirement 235
- Fixed decision_function calculation for FraudCost objective 254
- Fixed return value of Recall metrics 264

Changes
- 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

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


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.

Enhancements
- Adding basic pipeline structure visualization 211

Documentation Changes
- Added notebooks to build process 212

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 contributing instructions 213

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 guardrail 121
- Added additional regression objectives 100

Fixes
- Reordered `describe_pipeline` to make main objective first column 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