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[BACKPORT] Implements mars.learn.preprocessing.{LabelBinarizer, label_binarize} (#2415) #2421

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Sep 3, 2021
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[BACKPORT] Implements mars.learn.preprocessing.{LabelBinarizer, label_binarize} (#2415) #2421

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272 changes: 178 additions & 94 deletions docs/source/locale/zh_CN/LC_MESSAGES/reference/learn/reference.po
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17 changes: 17 additions & 0 deletions docs/source/reference/learn/reference.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -93,6 +93,21 @@ Ensemble Methods
ensemble.BlockwiseVotingClassifier
ensemble.BlockwiseVotingRegressor

.. _linear_model_ref:

Linear Models
=============

Classical linear regressors
---------------------------

.. currentmodule:: mars.learn

.. autosummary::
:toctree: generated/

linear_model.LinearRegression

.. _metrics_ref:

Metrics
Expand Down Expand Up @@ -198,8 +213,10 @@ Preprocessing and Normalization
.. autosummary::
:toctree: generated/

preprocessing.LabelBinarizer
preprocessing.MinMaxScaler
preprocessing.minmax_scale
preprocessing.label_binarize
preprocessing.normalize

.. _semi_supervised_ref:
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8 changes: 4 additions & 4 deletions mars/learn/contrib/tensorflow/dataset.py
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Expand Up @@ -35,8 +35,8 @@
@require_not_none(tf)
class MarsDataset:
def __init__(self, tensors,
output_shapes=None,
output_types=None,
output_shapes: Tuple[int, ...]=None,
output_types: Tuple[np.dtype, ...]=None,
fetch_kwargs=None):

self._context = get_context()
Expand Down Expand Up @@ -123,8 +123,8 @@ def make_generator(): # pragma: no cover


def gen_tensorflow_dataset(tensors,
output_shapes=None,
output_types=None,
output_shapes: Tuple[int, ...]=None,
output_types: Tuple[np.dtype, ...]=None,
fetch_kwargs=None):
"""
convert mars data type to tf.data.Dataset. Note this is based tensorflow 2.0
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9 changes: 3 additions & 6 deletions mars/learn/preprocessing/__init__.py
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Expand Up @@ -12,10 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.

try:
from ._data import MinMaxScaler
from ._data import minmax_scale
except ImportError: # pragma: no cover
# sklearn not installed
pass
from ._data import MinMaxScaler
from ._data import minmax_scale
from ._label import LabelBinarizer, label_binarize
from .normalize import normalize
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