Deprecate estimators

dask_glm/estimators.py

@@ -1,6 +1,8 @@
 """
 Models following scikit-learn's estimator API.
 """
+import warnings
+
 from sklearn.base import BaseEstimator
 
 from . import algorithms
@@ -10,6 +12,12 @@
     poisson_deviance
 )
 
+msg = ("The 'dask_glm.estimators' module is deprecated in favor of "
+       "'dask_ml.linear_models'. Please install 'dask-ml' and update "
+       "your imports.")
+
+warnings.warn(msg, FutureWarning)
+
 
 class _GLM(BaseEstimator):
     """ Base estimator for Generalized Linear Models

dask_glm/tests/test_estimators.py

@@ -1,106 +1,10 @@
 import pytest
 import dask
 
-from dask_glm.estimators import LogisticRegression, LinearRegression, PoissonRegression
-from dask_glm.datasets import make_classification, make_regression, make_poisson
-from dask_glm.regularizers import Regularizer
 
+def test_warns():
+    with pytest.warns(FutureWarning) as w:
+        import dask_glm.estimators  # noqa
 
-@pytest.fixture(params=[r() for r in Regularizer.__subclasses__()])
-def solver(request):
-    """Parametrized fixture for all the solver names"""
-    return request.param
-
-
-@pytest.fixture(params=[r() for r in Regularizer.__subclasses__()])
-def regularizer(request):
-    """Parametrized fixture for all the regularizer names"""
-    return request.param
-
-
-class DoNothingTransformer(object):
-    def fit(self, X, y=None):
-        return self
-
-    def transform(self, X, y=None):
-        return X
-
-    def fit_transform(self, X, y=None):
-        return X
-
-    def get_params(self, deep=True):
-        return {}
-
-
-X, y = make_classification()
-
-
-def test_lr_init(solver):
-    LogisticRegression(solver=solver)
-
-
-def test_pr_init(solver):
-    PoissonRegression(solver=solver)
-
-
-@pytest.mark.parametrize('fit_intercept', [True, False])
-def test_fit(fit_intercept):
-    X, y = make_classification(n_samples=100, n_features=5, chunksize=10)
-    lr = LogisticRegression(fit_intercept=fit_intercept)
-    lr.fit(X, y)
-    lr.predict(X)
-    lr.predict_proba(X)
-
-
-@pytest.mark.parametrize('fit_intercept', [True, False])
-def test_lm(fit_intercept):
-    X, y = make_regression(n_samples=100, n_features=5, chunksize=10)
-    lr = LinearRegression(fit_intercept=fit_intercept)
-    lr.fit(X, y)
-    lr.predict(X)
-    if fit_intercept:
-        assert lr.intercept_ is not None
-
-
-@pytest.mark.parametrize('fit_intercept', [True, False])
-def test_big(fit_intercept):
-    with dask.config.set(scheduler='synchronous'):
-        X, y = make_classification()
-        lr = LogisticRegression(fit_intercept=fit_intercept)
-        lr.fit(X, y)
-        lr.predict(X)
-        lr.predict_proba(X)
-    if fit_intercept:
-        assert lr.intercept_ is not None
-
-
-@pytest.mark.parametrize('fit_intercept', [True, False])
-def test_poisson_fit(fit_intercept):
-    with dask.config.set(scheduler='synchronous'):
-        X, y = make_poisson()
-        pr = PoissonRegression(fit_intercept=fit_intercept)
-        pr.fit(X, y)
-        pr.predict(X)
-        pr.get_deviance(X, y)
-    if fit_intercept:
-        assert pr.intercept_ is not None
-
-
-def test_in_pipeline():
-    from sklearn.pipeline import make_pipeline
-    X, y = make_classification(n_samples=100, n_features=5, chunksize=10)
-    pipe = make_pipeline(DoNothingTransformer(), LogisticRegression())
-    pipe.fit(X, y)
-
-
-def test_gridsearch():
-    from sklearn.pipeline import make_pipeline
-    dcv = pytest.importorskip('dask_searchcv')
-
-    X, y = make_classification(n_samples=100, n_features=5, chunksize=10)
-    grid = {
-        'logisticregression__lamduh': [.001, .01, .1, .5]
-    }
-    pipe = make_pipeline(DoNothingTransformer(), LogisticRegression())
-    search = dcv.GridSearchCV(pipe, grid, cv=3)
-    search.fit(X, y)
+    assert len(w)
+    assert 'dask-ml' in str(w[-1])

docs/api.rst

@@ -5,14 +5,6 @@
 API Reference
 -------------
 
-.. _api.estimators:
-
-Estimators
-==========
-
-.. automodule:: dask_glm.estimators
-   :members:
-
 .. _api.families:
 
 Families

docs/index.rst

@@ -9,13 +9,13 @@ Dask-glm
 *Dask-glm is a library for fitting Generalized Linear Models on large datasets*
 
 Dask-glm builds on the `dask`_ project to fit `GLM`_'s on datasets in parallel.
-It offers a `scikit-learn`_ compatible API for specifying your model.
+It provides the optimizers and regularizers used by libraries like `dask-ml`_,
+which builds scikit-learn-style APIs on top of those components.
 
 .. toctree::
    :maxdepth: 2
    :caption: Contents:
 
-   estimators
    examples
    api
 
@@ -30,3 +30,4 @@ Indices and tables
 .. _dask: http://dask.pydata.org/en/latest/
 .. _GLM: https://en.wikipedia.org/wiki/Generalized_linear_model
 .. _scikit-learn: http://scikit-learn.org/
+.. _dask-ml: http://dask-ml.readthedocs.org/