Update docstrings to import dask.ml

dask_ml/compose/_column_transformer.py

@@ -135,7 +135,7 @@ class ColumnTransformer(sklearn.compose.ColumnTransformer):
 
     Examples
     --------
-    >>> from dask_ml.compose import ColumnTransformer
+    >>> from dask.ml.compose import ColumnTransformer
     >>> from sklearn.preprocessing import Normalizer
     >>> ct = ColumnTransformer(
     ...     [("norm1", Normalizer(norm='l1'), [0, 1]),

dask_ml/datasets.py

@@ -142,7 +142,7 @@ def make_blobs(
 
     Examples
     --------
-    >>> from dask_ml.datasets import make_blobs
+    >>> from dask.ml.datasets import make_blobs
     >>> X, y = make_blobs(n_samples=100000, chunks=10000)
     >>> X
     dask.array<..., shape=(100000, 2), dtype=float64, chunksize=(10000, 2)>

dask_ml/decomposition/pca.py

@@ -144,7 +144,7 @@ class PCA(sklearn.decomposition.PCA):
     --------
     >>> import numpy as np
     >>> import dask.array as da
-    >>> from dask_ml.decomposition import PCA
+    >>> from dask.ml.decomposition import PCA
     >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
     >>> dX = da.from_array(X, chunks=X.shape)
     >>> pca = PCA(n_components=2)

dask_ml/decomposition/truncated_svd.py

@@ -100,7 +100,7 @@ def __init__(
 
         Examples
         --------
-        >>> from dask_ml.decomposition import TruncatedSVD
+        >>> from dask.ml.decomposition import TruncatedSVD
         >>> import dask.array as da
         >>> X = da.random.normal(size=(1000, 20), chunks=(100, 20))
         >>> svd = TruncatedSVD(n_components=5, n_iter=3, random_state=42)

dask_ml/feature_extraction/text.py

@@ -142,7 +142,7 @@ class CountVectorizer(sklearn.feature_extraction.text.CountVectorizer):
     The Dask-ML implementation currently requires that ``raw_documents``
     is a :class:`dask.bag.Bag` of documents (lists of strings).
 
-    >>> from dask_ml.feature_extraction.text import CountVectorizer
+    >>> from dask.ml.feature_extraction.text import CountVectorizer
     >>> import dask.bag as db
     >>> from distributed import Client
     >>> client = Client()

dask_ml/metrics/classification.py

@@ -60,7 +60,7 @@ def accuracy_score(
     --------
     >>> import dask.array as da
     >>> import numpy as np
-    >>> from dask_ml.metrics import accuracy_score
+    >>> from dask.ml.metrics import accuracy_score
     >>> y_pred = da.from_array(np.array([0, 2, 1, 3]), chunks=2)
     >>> y_true = da.from_array(np.array([0, 1, 2, 3]), chunks=2)
     >>> accuracy_score(y_true, y_pred)

dask_ml/model_selection/_hyperband.py

@@ -157,8 +157,8 @@ class HyperbandSearchCV(BaseIncrementalSearchCV):
     Examples
     --------
     >>> import numpy as np
-    >>> from dask_ml.model_selection import HyperbandSearchCV
-    >>> from dask_ml.datasets import make_classification
+    >>> from dask.ml.model_selection import HyperbandSearchCV
+    >>> from dask.ml.datasets import make_classification
     >>> from sklearn.linear_model import SGDClassifier
     >>>
     >>> X, y = make_classification(chunks=20)

dask_ml/model_selection/_incremental.py

@@ -411,7 +411,7 @@ async def fit(
     Examples
     --------
     >>> import numpy as np
-    >>> from dask_ml.datasets import make_classification
+    >>> from dask.ml.datasets import make_classification
     >>> X, y = make_classification(n_samples=5000000, n_features=20,
     ...                            chunks=100000, random_state=0)
 
@@ -443,7 +443,7 @@ async def fit(
     >>> from dask.distributed import Client
     >>> client = Client(processes=False)
 
-    >>> from dask_ml.model_selection._incremental import fit
+    >>> from dask.ml.model_selection._incremental import fit
     >>> info, models, history, best = fit(model, params,
     ...                                   X_train, y_train,
     ...                                   X_test, y_test,
@@ -920,7 +920,7 @@ class IncrementalSearchCV(BaseIncrementalSearchCV):
     >>> from dask.distributed import Client
     >>> client = Client()
     >>> import numpy as np
-    >>> from dask_ml.datasets import make_classification
+    >>> from dask.ml.datasets import make_classification
     >>> X, y = make_classification(n_samples=5000000, n_features=20,
     ...                            chunks=100000, random_state=0)
 

dask_ml/model_selection/_split.py

@@ -407,7 +407,7 @@ def train_test_split(
     Examples
     --------
     >>> import dask.array as da
-    >>> from dask_ml.datasets import make_regression
+    >>> from dask.ml.datasets import make_regression
 
     >>> X, y = make_regression(n_samples=125, n_features=4, chunks=50,
     ...                        random_state=0)

dask_ml/naive_bayes.py

@@ -11,8 +11,8 @@ class GaussianNB(BaseEstimator):
 
     Examples
     --------
-    >>> from dask_ml import datasets
-    >>> from dask_ml.naive_bayes import GaussianNB
+    >>> from dask.ml import datasets
+    >>> from dask.ml.naive_bayes import GaussianNB
     >>> X, y = datasets.make_classification(chunks=50)
     >>> gnb = GaussianNB()
     >>> gnb.fit(X, y)

dask_ml/preprocessing/_block_transformer.py

@@ -37,7 +37,7 @@ class BlockTransformer(BaseEstimator, TransformerMixin):
 
     >>> import dask.datasets
     >>> import pandas as pd
-    >>> from dask_ml.preprocessing import BlockTransformer
+    >>> from dask.ml.preprocessing import BlockTransformer
     >>> df = dask.datasets.timeseries()
     >>> df
     ... # doctest: +SKIP

dask_ml/preprocessing/_encoders.py

@@ -89,7 +89,7 @@ class OneHotEncoder(DaskMLBaseMixin, sklearn.preprocessing.OneHotEncoder):
     Given a dataset with two features, we let the encoder find the unique
     values per feature and transform the data to a binary one-hot encoding.
 
-    >>> from dask_ml.preprocessing import OneHotEncoder
+    >>> from dask.ml.preprocessing import OneHotEncoder
     >>> import numpy as np
     >>> import dask.array as da
     >>> enc = OneHotEncoder()

dask_ml/preprocessing/label.py

@@ -46,7 +46,7 @@ class LabelEncoder(sklearn.preprocessing.LabelEncoder):
     --------
     `LabelEncoder` can be used to normalize labels.
 
-    >>> from dask_ml import preprocessing
+    >>> from dask.ml import preprocessing
     >>> le = preprocessing.LabelEncoder()
     >>> le.fit([1, 2, 2, 6])
     LabelEncoder()

dask_ml/wrappers.py

@@ -436,8 +436,8 @@ class Incremental(ParallelPostFit):
 
     Examples
     --------
-    >>> from dask_ml.wrappers import Incremental
-    >>> from dask_ml.datasets import make_classification
+    >>> from dask.ml.wrappers import Incremental
+    >>> from dask.ml.datasets import make_classification
     >>> import sklearn.linear_model
     >>> X, y = make_classification(chunks=25)
     >>> est = sklearn.linear_model.SGDClassifier()