Source code for mars.learn.metrics._classification

# Copyright 1999-2020 Alibaba Group Holding Ltd.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np

from ... import opcodes as OperandDef
from ... import tensor as mt
from ...core import ENTITY_TYPE, TilesError
from ...context import get_context
from ...serialize import AnyField, BoolField, KeyField
from ...tensor.core import TensorOrder
from ...utils import recursive_tile
from ..operands import LearnOperand, LearnOperandMixin, OutputType
from ._check_targets import _check_targets

class AccuracyScore(LearnOperand, LearnOperandMixin):
    _op_type_ = OperandDef.ACCURACY_SCORE

    _y_true = AnyField('y_true')
    _y_pred = AnyField('y_pred')
    _normalize = BoolField('normalize')
    _sample_weight = AnyField('sample_weight')
    _type_true = KeyField('type_true')

    def __init__(self, y_true=None, y_pred=None, normalize=None,
                 sample_weight=None, type_true=None, **kw):
        super().__init__(_y_true=y_true, _y_pred=y_pred,
                         _normalize=normalize, _sample_weight=sample_weight,
                         _type_true=type_true, **kw)
        self.output_types = [OutputType.tensor]

    def y_true(self):
        return self._y_true

    def y_pred(self):
        return self._y_pred

    def normalize(self):
        return self._normalize

    def sample_weight(self):
        return self._sample_weight

    def type_true(self):
        return self._type_true

    def _set_inputs(self, inputs):
        inputs_iter = iter(self._inputs)
        if self._y_true is not None:
            self._y_true = next(inputs_iter)
        if self._y_pred is not None:
            self._y_pred = next(inputs_iter)
        if self._type_true is not None:
            self._type_true = next(inputs_iter)
        if isinstance(self._sample_weight, ENTITY_TYPE):
            self._sample_weight = next(inputs_iter)

    def __call__(self, y_true, y_pred):
        type_true, y_true, y_pred = _check_targets(y_true, y_pred)
        self._type_true = type_true
        inputs = [y_true, y_pred, type_true]
        if isinstance(self._sample_weight, ENTITY_TYPE):

        dtype = np.dtype(float) if self._normalize else np.result_type(y_true, y_pred)
        return self.new_tileable(inputs, dtype=dtype,
                                 shape=(), order=TensorOrder.C_ORDER)

    def tile(cls, op):
        ctx = get_context()
            type_true = ctx.get_chunk_results([op.type_true.chunks[0].key])[0]
        except (KeyError, AttributeError):
            raise TilesError('type_true needed to be executed first')

        y_true, y_pred = op.y_true, op.y_pred
        if type_true.item().startswith('multilabel'):
            differing_labels = mt.count_nonzero(y_true - y_pred, axis=1)
            score = mt.equal(differing_labels, 0)
            score = mt.equal(y_true, y_pred)

        result = _weighted_sum(score, op.sample_weight, op.normalize)
        return [recursive_tile(result)]

def _weighted_sum(sample_score, sample_weight, normalize=False):
    if normalize:
        return mt.average(sample_score, weights=sample_weight)
    elif sample_weight is not None:
        return, sample_weight)
        return sample_score.sum()

[docs]def accuracy_score(y_true, y_pred, normalize=True, sample_weight=None, session=None, run_kwargs=None): """Accuracy classification score. In multilabel classification, this function computes subset accuracy: the set of labels predicted for a sample must *exactly* match the corresponding set of labels in y_true. Read more in the :ref:`User Guide <accuracy_score>`. Parameters ---------- y_true : 1d array-like, or label indicator tensor / sparse tensor Ground truth (correct) labels. y_pred : 1d array-like, or label indicator tensor / sparse tensor Predicted labels, as returned by a classifier. normalize : bool, optional (default=True) If ``False``, return the number of correctly classified samples. Otherwise, return the fraction of correctly classified samples. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- score : float If ``normalize == True``, return the fraction of correctly classified samples (float), else returns the number of correctly classified samples (int). The best performance is 1 with ``normalize == True`` and the number of samples with ``normalize == False``. See also -------- jaccard_score, hamming_loss, zero_one_loss Notes ----- In binary and multiclass classification, this function is equal to the ``jaccard_score`` function. Examples -------- >>> from mars.learn.metrics import accuracy_score >>> y_pred = [0, 2, 1, 3] >>> y_true = [0, 1, 2, 3] >>> accuracy_score(y_true, y_pred).execute() 0.5 >>> accuracy_score(y_true, y_pred, normalize=False).execute() 2 In the multilabel case with binary label indicators: >>> import mars.tensor as mt >>> accuracy_score(mt.array([[0, 1], [1, 1]]), mt.ones((2, 2))).execute() 0.5 """ # Compute accuracy for each possible representation op = AccuracyScore(y_true=y_true, y_pred=y_pred, normalize=normalize, sample_weight=sample_weight) score = op(y_true, y_pred) return score.execute(session=session, **(run_kwargs or dict()))