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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# Unless required by applicable law or agreed to in writing, software
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from typing import Union
import numpy as np
import scipy.sparse as sp
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils.sparsefuncs import min_max_axis
from ... import execute as execute_tileable, fetch as fetch_tileable
from ... import opcodes
from ... import tensor as mt
from ...core import ENTITY_TYPE, OutputType, recursive_tile
from ...core.context import get_context, Context
from ...lib.sparse import SparseNDArray
from ...serialization.serializables import AnyField, BoolField, Int32Field, StringField
from ...tensor.core import TensorOrder
from ...typing import TileableType
from ...utils import has_unknown_shape
from ..operands import LearnOperand, LearnOperandMixin
from ..utils import column_or_1d
from ..utils._encode import _unique, _encode
from ..utils.multiclass import unique_labels, type_of_target
from ..utils.validation import _num_samples, check_is_fitted, check_array
[docs]class LabelEncoder(TransformerMixin, BaseEstimator):
"""Encode target labels with value between 0 and n_classes-1.
This transformer should be used to encode target values, *i.e.* `y`, and
not the input `X`.
Read more in the :ref:`User Guide <preprocessing_targets>`.
Attributes
----------
classes_ : ndarray of shape (n_classes,)
Holds the label for each class.
See Also
--------
OrdinalEncoder : Encode categorical features using an ordinal encoding
scheme.
OneHotEncoder : Encode categorical features as a one-hot numeric array.
Examples
--------
`LabelEncoder` can be used to normalize labels.
>>> from sklearn import preprocessing
>>> le = preprocessing.LabelEncoder()
>>> le.fit([1, 2, 2, 6])
LabelEncoder()
>>> le.classes_
array([1, 2, 6])
>>> le.transform([1, 1, 2, 6])
array([0, 0, 1, 2]...)
>>> le.inverse_transform([0, 0, 1, 2])
array([1, 1, 2, 6])
It can also be used to transform non-numerical labels (as long as they are
hashable and comparable) to numerical labels.
>>> le = preprocessing.LabelEncoder()
>>> le.fit(["paris", "paris", "tokyo", "amsterdam"])
LabelEncoder()
>>> list(le.classes_)
['amsterdam', 'paris', 'tokyo']
>>> le.transform(["tokyo", "tokyo", "paris"])
array([2, 2, 1]...)
>>> list(le.inverse_transform([2, 2, 1]))
['tokyo', 'tokyo', 'paris']
"""
def fit(self, y, session=None, run_kwargs=None, execute=True):
"""Fit label encoder.
Parameters
----------
y : array-like of shape (n_samples,)
Target values.
Returns
-------
self : returns an instance of self.
Fitted label encoder.
"""
y = column_or_1d(y, warn=True)
self.classes_ = _unique(y)
if execute:
self.classes_ = execute_tileable(
self.classes_, session=session, **(run_kwargs or dict())
)
return self
def fit_transform(self, y, session=None, run_kwargs=None):
"""Fit label encoder and return encoded labels.
Parameters
----------
y : array-like of shape (n_samples,)
Target values.
Returns
-------
y : array-like of shape (n_samples,)
Encoded labels.
"""
y = column_or_1d(y, warn=True)
self.classes_, y = execute_tileable(
_unique(y, return_inverse=True), session=session, **(run_kwargs or dict())
)
return y
def transform(self, y, session=None, run_kwargs=None, execute=True):
"""Transform labels to normalized encoding.
Parameters
----------
y : array-like of shape (n_samples,)
Target values.
Returns
-------
y : array-like of shape (n_samples,)
Labels as normalized encodings.
"""
check_is_fitted(self)
y = column_or_1d(y, warn=True)
# transform of empty array is empty array
if _num_samples(y) == 0:
return mt.array([])
t = _encode(y, uniques=self.classes_)
if execute:
t = t.execute(session=session, **(run_kwargs or dict()))
return t
def inverse_transform(self, y, session=None, run_kwargs=None):
"""Transform labels back to original encoding.
Parameters
----------
y : ndarray of shape (n_samples,)
Target values.
Returns
-------
y : ndarray of shape (n_samples,)
Original encoding.
"""
check_is_fitted(self)
y = column_or_1d(y, warn=True)
# inverse transform of empty array is empty array
if _num_samples(y) == 0:
return mt.array([])
def _class_checker(chunk_data, classes_data):
diff = np.setdiff1d(chunk_data, np.arange(len(classes_data)))
if len(diff):
raise ValueError("y contains previously unseen labels: %s" % str(diff))
return chunk_data
y = mt.asarray(y).map_chunk(_class_checker, args=(self.classes_,))
return self.classes_[y].execute(session=session, **(run_kwargs or dict()))
def _more_tags(self):
return {"X_types": ["1dlabels"]}
[docs]class LabelBinarizer(TransformerMixin, BaseEstimator):
"""Binarize labels in a one-vs-all fashion.
Several regression and binary classification algorithms are
available in scikit-learn. A simple way to extend these algorithms
to the multi-class classification case is to use the so-called
one-vs-all scheme.
At learning time, this simply consists in learning one regressor
or binary classifier per class. In doing so, one needs to convert
multi-class labels to binary labels (belong or does not belong
to the class). LabelBinarizer makes this process easy with the
transform method.
At prediction time, one assigns the class for which the corresponding
model gave the greatest confidence. LabelBinarizer makes this easy
with the inverse_transform method.
Read more in the :ref:`User Guide <preprocessing_targets>`.
Parameters
----------
neg_label : int, default=0
Value with which negative labels must be encoded.
pos_label : int, default=1
Value with which positive labels must be encoded.
sparse_output : bool, default=False
True if the returned array from transform is desired to be in sparse
CSR format.
Attributes
----------
classes_ : ndarray of shape (n_classes,)
Holds the label for each class.
y_type_ : str
Represents the type of the target data as evaluated by
utils.multiclass.type_of_target. Possible type are 'continuous',
'continuous-multioutput', 'binary', 'multiclass',
'multiclass-multioutput', 'multilabel-indicator', and 'unknown'.
sparse_input_ : bool
True if the input data to transform is given as a sparse matrix, False
otherwise.
Examples
--------
>>> from mars.learn import preprocessing
>>> lb = preprocessing.LabelBinarizer()
>>> lb.fit([1, 2, 6, 4, 2])
LabelBinarizer()
>>> lb.classes_
array([1, 2, 4, 6])
>>> lb.transform([1, 6])
array([[1, 0, 0, 0],
[0, 0, 0, 1]])
Binary targets transform to a column vector
>>> lb = preprocessing.LabelBinarizer()
>>> lb.fit_transform(['yes', 'no', 'no', 'yes'])
array([[1],
[0],
[0],
[1]])
Passing a 2D matrix for multilabel classification
>>> import numpy as np
>>> lb.fit(np.array([[0, 1, 1], [1, 0, 0]]))
LabelBinarizer()
>>> lb.classes_
array([0, 1, 2])
>>> lb.transform([0, 1, 2, 1])
array([[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[0, 1, 0]])
See Also
--------
label_binarize : Function to perform the transform operation of
LabelBinarizer with fixed classes.
OneHotEncoder : Encode categorical features using a one-hot aka one-of-K
scheme.
"""
[docs] def __init__(self, *, neg_label=0, pos_label=1, sparse_output=False):
if neg_label >= pos_label:
raise ValueError(
"neg_label={0} must be strictly less than "
"pos_label={1}.".format(neg_label, pos_label)
)
if sparse_output and (pos_label == 0 or neg_label != 0):
raise ValueError(
"Sparse binarization is only supported with non "
"zero pos_label and zero neg_label, got "
"pos_label={0} and neg_label={1}"
"".format(pos_label, neg_label)
)
self.neg_label = neg_label
self.pos_label = pos_label
self.sparse_output = sparse_output
def fit(self, y, session=None, run_kwargs=None):
"""Fit label binarizer.
Parameters
----------
y : ndarray of shape (n_samples,) or (n_samples, n_classes)
Target values. The 2-d matrix should only contain 0 and 1,
represents multilabel classification.
Returns
-------
self : returns an instance of self.
"""
self.y_type_ = fetch_tileable(
execute_tileable(
type_of_target(y), session=session, **(run_kwargs or dict())
)
)
if "multioutput" in self.y_type_:
raise ValueError(
"Multioutput target data is not supported with " "label binarization"
)
if _num_samples(y) == 0: # pragma: no cover
raise ValueError("y has 0 samples: %r" % y)
self.sparse_input_ = mt.tensor(y).issparse()
self.classes_ = unique_labels(y).execute(
session=session, **(run_kwargs or dict())
)
return self
def fit_transform(self, y, session=None, run_kwargs=None):
"""Fit label binarizer and transform multi-class labels to binary
labels.
The output of transform is sometimes referred to as
the 1-of-K coding scheme.
Parameters
----------
y : {ndarray, sparse matrix} of shape (n_samples,) or \
(n_samples, n_classes)
Target values. The 2-d matrix should only contain 0 and 1,
represents multilabel classification. Sparse matrix can be
CSR, CSC, COO, DOK, or LIL.
Returns
-------
Y : {ndarray, sparse matrix} of shape (n_samples, n_classes)
Shape will be (n_samples, 1) for binary problems. Sparse matrix
will be of CSR format.
"""
return self.fit(y, session=session, run_kwargs=run_kwargs).transform(
y, session=session, run_kwargs=run_kwargs
)
def transform(self, y, session=None, run_kwargs=None):
"""Transform multi-class labels to binary labels.
The output of transform is sometimes referred to by some authors as
the 1-of-K coding scheme.
Parameters
----------
y : {array, sparse matrix} of shape (n_samples,) or \
(n_samples, n_classes)
Target values. The 2-d matrix should only contain 0 and 1,
represents multilabel classification. Sparse matrix can be
CSR, CSC, COO, DOK, or LIL.
Returns
-------
Y : {ndarray, sparse matrix} of shape (n_samples, n_classes)
Shape will be (n_samples, 1) for binary problems. Sparse matrix
will be of CSR format.
"""
check_is_fitted(self)
target = fetch_tileable(
execute_tileable(
type_of_target(y), session=session, **(run_kwargs or dict())
)
)
y_is_multilabel = target.startswith("multilabel")
if y_is_multilabel and not self.y_type_.startswith("multilabel"):
raise ValueError("The object was not fitted with multilabel" " input.")
return label_binarize(
y,
classes=self.classes_,
pos_label=self.pos_label,
neg_label=self.neg_label,
sparse_output=self.sparse_output,
)
def inverse_transform(self, Y, threshold=None):
"""Transform binary labels back to multi-class labels.
Parameters
----------
Y : {ndarray, sparse matrix} of shape (n_samples, n_classes)
Target values. All sparse matrices are converted to CSR before
inverse transformation.
threshold : float, default=None
Threshold used in the binary and multi-label cases.
Use 0 when ``Y`` contains the output of decision_function
(classifier).
Use 0.5 when ``Y`` contains the output of predict_proba.
If None, the threshold is assumed to be half way between
neg_label and pos_label.
Returns
-------
y : {ndarray, sparse matrix} of shape (n_samples,)
Target values. Sparse matrix will be of CSR format.
Notes
-----
In the case when the binary labels are fractional
(probabilistic), inverse_transform chooses the class with the
greatest value. Typically, this allows to use the output of a
linear model's decision_function method directly as the input
of inverse_transform.
"""
check_is_fitted(self)
if threshold is None:
threshold = (self.pos_label + self.neg_label) / 2.0
Y = mt.asarray(Y)
if self.y_type_ == "multiclass":
y_inv = Y.map_chunk(
_inverse_binarize_multiclass,
args=(self.classes_,),
dtype=self.classes_.dtype,
shape=(Y.shape[0],),
)
else:
shape = (Y.shape[0],) if self.y_type_ != "multilabel-indicator" else Y.shape
y_inv = Y.map_chunk(
_inverse_binarize_thresholding,
args=(self.y_type_, self.classes_, threshold),
dtype=self.classes_.dtype,
shape=shape,
)
if self.sparse_input_:
y_inv = y_inv.tosparse()
elif y_inv.issparse():
y_inv = y_inv.todense()
return y_inv
def _more_tags(
self,
): # pragma: no cover # noqa: R0201 # pylint: disable=no-self-use
return {"X_types": ["1dlabels"]}
class LabelBinarize(LearnOperand, LearnOperandMixin):
_op_type_ = opcodes.LABEL_BINARIZE
y = AnyField("y")
classes = AnyField("classes")
neg_label = Int32Field("neg_label")
pos_label = Int32Field("pos_label")
sparse_output = BoolField("sparse_output")
# for chunk
y_type = StringField("y_type")
pos_switch = BoolField("pos_switch")
def __call__(self, y: TileableType, classes: TileableType):
inputs = []
if isinstance(y, ENTITY_TYPE):
inputs.append(y)
if isinstance(classes, ENTITY_TYPE):
inputs.append(classes)
self.sparse = self.sparse_output
self.output_types = [OutputType.tensor]
return self.new_tileable(
inputs, shape=(np.nan,), dtype=np.dtype(int), order=TensorOrder.C_ORDER
)
def _set_inputs(self, inputs):
super()._set_inputs(inputs)
if isinstance(self.y, ENTITY_TYPE):
self.y = self._inputs[0]
if isinstance(self.classes, ENTITY_TYPE):
self.classes = self._inputs[-1]
@classmethod
def tile(cls, op: "LabelBinarize"):
y = op.y
classes = op.classes
neg_label = op.neg_label
pos_label = op.pos_label
sparse_output = op.sparse_output
out = op.outputs[0]
ctx = get_context()
if (isinstance(y, ENTITY_TYPE) and has_unknown_shape(y)) or (
isinstance(classes, ENTITY_TYPE) and has_unknown_shape(classes)
): # pragma: no cover
yield
if (
isinstance(classes, ENTITY_TYPE) and len(classes.chunks) > 1
): # pragma: no cover
classes = yield from recursive_tile(classes.rechunk(classes.shape))
if not isinstance(y, list):
# XXX Workaround that will be removed when list of list format is
# dropped
y = check_array(y, accept_sparse=True, ensure_2d=False, dtype=None)
else:
if _num_samples(y) == 0:
raise ValueError("y has 0 samples: %r" % y)
y = yield from recursive_tile(mt.tensor(y))
if neg_label >= pos_label:
raise ValueError(
"neg_label={0} must be strictly less than "
"pos_label={1}.".format(neg_label, pos_label)
)
if sparse_output and (pos_label == 0 or neg_label != 0):
raise ValueError(
"Sparse binarization is only supported with non "
"zero pos_label and zero neg_label, got "
"pos_label={0} and neg_label={1}"
"".format(pos_label, neg_label)
)
# To account for pos_label == 0 in the dense case
pos_switch = pos_label == 0
if pos_switch:
pos_label = -neg_label
y_type = yield from recursive_tile(type_of_target(y))
yield y_type.chunks
y_type = ctx.get_chunks_result([y_type.chunks[0].key])[0]
y_type = y_type.item() if hasattr(y_type, "item") else y_type
if "multioutput" in y_type:
raise ValueError(
"Multioutput target data is not supported with label " "binarization"
)
if y_type == "unknown":
raise ValueError("The type of target data is not known")
n_samples = mt.tensor(y).shape[0]
n_classes = len(classes)
if y_type == "binary":
if n_classes == 1:
if sparse_output:
return (
yield from recursive_tile(
mt.zeros((n_samples, 1), dtype=int, sparse=True)
)
)
else:
Y = mt.zeros((len(y), 1), dtype=int)
Y += neg_label
return (yield from recursive_tile(Y))
elif len(classes) >= 3:
y_type = "multiclass"
if y_type == "multilabel-indicator":
y_n_classes = y.shape[1] if hasattr(y, "shape") else len(y[0])
if mt.tensor(classes).size != y_n_classes:
raise ValueError(
"classes {0} mismatch with the labels {1}"
" found in the data".format(classes, unique_labels(y))
)
if y_type in ("binary", "multiclass"):
y = yield from recursive_tile(column_or_1d(y))
if y_type == "binary":
out_shape = (n_samples, 1)
else:
out_shape = (n_samples, n_classes)
elif y_type == "multilabel-indicator":
out_shape = y.shape
else:
raise ValueError(
"%s target data is not supported with label " "binarization" % y_type
)
out_chunks = []
for y_chunk in y.chunks:
chunk_inputs = [y_chunk]
classes_chunk = classes
if isinstance(classes, ENTITY_TYPE):
chunk_inputs.append(classes.chunks[0])
classes_chunk = classes.chunks[0]
chunk_op = LabelBinarize(
y=y_chunk,
classes=classes_chunk,
neg_label=neg_label,
pos_label=pos_label,
sparse_output=sparse_output,
y_type=y_type,
pos_switch=pos_switch,
_output_types=op.output_types,
)
if len(out_shape) == 2:
chunk_shape = (y_chunk.shape[0], out_shape[1])
chunk_index = (y_chunk.index[0], 0)
else: # pragma: no cover
chunk_shape = (y_chunk.shape[0],)
chunk_index = (y_chunk.index[0],)
out_chunk = chunk_op.new_chunk(
chunk_inputs,
shape=chunk_shape,
dtype=out.dtype,
order=out.order,
index=chunk_index,
)
out_chunks.append(out_chunk)
params = out.params.copy()
params["chunks"] = out_chunks
params["shape"] = out_shape
if len(out_shape) == 2:
nsplits = (y.nsplits[0], (out_shape[1],))
else: # pragma: no cover
nsplits = (y.nsplits[0],)
params["nsplits"] = nsplits
return op.copy().new_tileables(op.inputs, kws=[params])
@classmethod
def execute(cls, ctx: Union[dict, Context], op: "LabelBinarize"):
y = ctx[op.y.key]
if hasattr(y, "raw"):
# SparseNDArray
y = y.raw
if isinstance(op.classes, ENTITY_TYPE):
classes = ctx[op.classes.key]
else:
classes = op.classes
y_type = op.y_type
sparse_output = op.sparse_output
pos_label = op.pos_label
neg_label = op.neg_label
pos_switch = op.pos_switch
n_samples = y.shape[0] if sp.issparse(y) else len(y)
n_classes = len(classes)
sorted_class = np.sort(classes)
if y_type in ("binary", "multiclass"):
# pick out the known labels from y
y_in_classes = np.in1d(y, classes)
y_seen = y[y_in_classes]
indices = np.searchsorted(sorted_class, y_seen)
indptr = np.hstack((0, np.cumsum(y_in_classes)))
data = np.empty_like(indices)
data.fill(pos_label)
Y = sp.csr_matrix((data, indices, indptr), shape=(n_samples, n_classes))
elif y_type == "multilabel-indicator":
Y = sp.csr_matrix(y)
if pos_label != 1:
data = np.empty_like(Y.data)
data.fill(pos_label)
Y.data = data
else: # pragma: no cover
raise ValueError(
"%s target data is not supported with label " "binarization" % y_type
)
if not sparse_output:
Y = Y.toarray()
Y = Y.astype(int, copy=False)
if neg_label != 0:
Y[Y == 0] = neg_label
if pos_switch:
Y[Y == pos_label] = 0
else:
Y.data = Y.data.astype(int, copy=False)
# preserve label ordering
if np.any(classes != sorted_class):
indices = np.searchsorted(sorted_class, classes)
Y = Y[:, indices]
if y_type == "binary":
if sparse_output:
Y = Y.getcol(-1)
else:
Y = Y[:, -1].reshape((-1, 1))
if sp.issparse(Y):
Y = SparseNDArray(Y)
ctx[op.outputs[0].key] = Y
[docs]def label_binarize(
y, *, classes, neg_label=0, pos_label=1, sparse_output=False, execute=True
):
"""Binarize labels in a one-vs-all fashion.
Several regression and binary classification algorithms are
available in scikit-learn. A simple way to extend these algorithms
to the multi-class classification case is to use the so-called
one-vs-all scheme.
This function makes it possible to compute this transformation for a
fixed set of class labels known ahead of time.
Parameters
----------
y : array-like
Sequence of integer labels or multilabel data to encode.
classes : array-like of shape (n_classes,)
Uniquely holds the label for each class.
neg_label : int, default=0
Value with which negative labels must be encoded.
pos_label : int, default=1
Value with which positive labels must be encoded.
sparse_output : bool, default=False,
Set to true if output binary array is desired in CSR sparse format.
Returns
-------
Y : {tensor, sparse tensor} of shape (n_samples, n_classes)
Shape will be (n_samples, 1) for binary problems.
Examples
--------
>>> from mars.learn.preprocessing import label_binarize
>>> label_binarize([1, 6], classes=[1, 2, 4, 6])
array([[1, 0, 0, 0],
[0, 0, 0, 1]])
The class ordering is preserved:
>>> label_binarize([1, 6], classes=[1, 6, 4, 2])
array([[1, 0, 0, 0],
[0, 1, 0, 0]])
Binary targets transform to a column vector
>>> label_binarize(['yes', 'no', 'no', 'yes'], classes=['no', 'yes'])
array([[1],
[0],
[0],
[1]])
See Also
--------
LabelBinarizer : Class used to wrap the functionality of label_binarize and
allow for fitting to classes independently of the transform operation.
"""
op = LabelBinarize(
y=y,
classes=classes,
neg_label=neg_label,
pos_label=pos_label,
sparse_output=sparse_output,
)
result = op(y, classes)
return result.execute() if execute else result
def _inverse_binarize_multiclass(y, classes): # pragma: no cover
"""Inverse label binarization transformation for multiclass.
Multiclass uses the maximal score instead of a threshold.
"""
classes = np.asarray(classes)
if sp.issparse(y):
# Find the argmax for each row in y where y is a CSR matrix
y = y.tocsr()
n_samples, n_outputs = y.shape
outputs = np.arange(n_outputs)
row_max = min_max_axis(y, 1)[1]
row_nnz = np.diff(y.indptr)
y_data_repeated_max = np.repeat(row_max, row_nnz)
# picks out all indices obtaining the maximum per row
y_i_all_argmax = np.flatnonzero(y_data_repeated_max == y.data)
# For corner case where last row has a max of 0
if row_max[-1] == 0:
y_i_all_argmax = np.append(y_i_all_argmax, [len(y.data)])
# Gets the index of the first argmax in each row from y_i_all_argmax
index_first_argmax = np.searchsorted(y_i_all_argmax, y.indptr[:-1])
# first argmax of each row
y_ind_ext = np.append(y.indices, [0])
y_i_argmax = y_ind_ext[y_i_all_argmax[index_first_argmax]]
# Handle rows of all 0
y_i_argmax[np.where(row_nnz == 0)[0]] = 0
# Handles rows with max of 0 that contain negative numbers
samples = np.arange(n_samples)[(row_nnz > 0) & (row_max.ravel() == 0)]
for i in samples:
ind = y.indices[y.indptr[i] : y.indptr[i + 1]]
y_i_argmax[i] = classes[np.setdiff1d(outputs, ind)][0]
return classes[y_i_argmax]
else:
return classes.take(y.argmax(axis=1), mode="clip")
def _inverse_binarize_thresholding(
y, output_type, classes, threshold
): # pragma: no cover
"""Inverse label binarization transformation using thresholding."""
if output_type == "binary" and y.ndim == 2 and y.shape[1] > 2:
raise ValueError("output_type='binary', but y.shape = {0}".format(y.shape))
if output_type != "binary" and y.shape[1] != len(classes):
raise ValueError(
"The number of class is not equal to the number of " "dimension of y."
)
classes = np.asarray(classes)
# Perform thresholding
if sp.issparse(y):
if threshold > 0:
if y.format not in ("csr", "csc"):
y = y.tocsr()
y.data = np.array(y.data > threshold, dtype=int)
y.eliminate_zeros()
else:
y = np.array(y.toarray() > threshold, dtype=int)
else:
y = np.array(y > threshold, dtype=int)
# Inverse transform data
if output_type == "binary":
if sp.issparse(y):
y = y.toarray()
if y.ndim == 2 and y.shape[1] == 2:
return classes[y[:, 1]]
else:
if len(classes) == 1:
return np.repeat(classes[0], len(y))
else:
return classes[y.ravel()]
elif output_type == "multilabel-indicator":
return y
else:
raise ValueError("{0} format is not supported".format(output_type))