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* Add annotations for metrics module
Co-authored-by: dheim <physics@d-heim.de> Co-authored-by: Yuuichi ASAHI <asahi.yuuichi@qst.go.jp>
* DOC: make dataframe conversion more clear
* DOC: better examples * MAINT: fix typo * Fix XGBoost link
* basic make_classification as dask.DataFrame * returning a tuple of (DataFrame, Series)
…pted" (dask#653) * MAINT: better chunk warning in utils.py
* Added annotations to preprocessing * remove mypy warnings * Correct: import dask.dataframe * No errors * __typing * Resolve more errors * Flake8 and Isort errors * Nits * Nits * Nits * Return type added * Nits * Callable
* Add blockwise estimators
```python
In [1]: import sklearn.linear_model
...: import dask_ml.datasets
...: import dask_ml.ensemble
...:
...: X, y = dask_ml.datasets.make_classification(n_features=20, chunks=25)
...:
...: clf = dask_ml.ensemble.BlockwiseVotingClassifier(
...: sklearn.linear_model.LogisticRegression(), voting="soft",
...: classes=[0, 1]
...: )
...:
...: clf.fit(X, y)
In [2]: clf.estimators_
Out[2]:
[LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='auto', n_jobs=None, penalty='l2',
random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
warm_start=False),
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='auto', n_jobs=None, penalty='l2',
random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
warm_start=False),
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='auto', n_jobs=None, penalty='l2',
random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
warm_start=False),
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='auto', n_jobs=None, penalty='l2',
random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
warm_start=False)]
```
* DOC: clarify model selection issues * Added InverseDecaySearchCV
* DOC: 1.4.0 * fixup
* CI: Added 3.8 * bump to 38 * compat fixups * impute n_features_in * pca n_features_in_ * more compat * more compat * compat * new URL * require 0.23+ * require 0.23+ * fixup * cleanup * fix iid * trigger ci * linting * xfail * xfail * more xfail
* DEPR: Remove previously deprecated Partials
* Log amount of found clusters in kmeans init
Only within-block shuffling is allowed. This is the default, regardless of the types.
* Support Dask Dataframes in Hyperband
* Update changelog for 1.6.0
…-ml-1.6.0 RLS: v.1.6.0
jamesbsilva
approved these changes
Aug 11, 2020
jamesbsilva
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From what I can tell the parts related to CV are just adding typing and doctor stuff so I think should be compatible especially if all tests pass
tian-yi-zefr
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is there any particular files we need to review?
Author
Nothing in particular, I've looked through most of the changes and I don't suspect any to be breaking. Some notable additions: |
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Brings our ZEFR-INC/dask-ml fork of dask/dask-ml up to date with dask/dask-ml's v.1.6.0.