Source code for mars.tensor.base.where

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 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 itertools

import numpy as np

from ... import opcodes as OperandDef
from ...serialization.serializables import KeyField
from ...utils import has_unknown_shape
from ..utils import broadcast_shape, unify_chunks
from ..array_utils import as_same_device, device
from ..core import TENSOR_TYPE
from ..operands import TensorOperand, TensorOperandMixin
from ..datasource import tensor as astensor
from .broadcast_to import broadcast_to

class TensorWhere(TensorOperand, TensorOperandMixin):
    _op_type_ = OperandDef.WHERE

    _condition = KeyField("condition")
    _x = KeyField("x")
    _y = KeyField("y")

    def condition(self):
        return self._condition

    def x(self):
        return self._x

    def y(self):
        return self._y

    def _set_inputs(self, inputs):
        self._condition = self._inputs[0]
        self._x = self._inputs[1]
        self._y = self._inputs[2]

    def __call__(self, condition, x, y, shape=None):
        shape = shape or broadcast_shape(condition.shape, x.shape, y.shape)
        return self.new_tensor([condition, x, y], shape)

    def tile(cls, op):
        if has_unknown_shape(*op.inputs):
        inputs = yield from unify_chunks(
            *[(input, list(range(input.ndim))[::-1]) for input in op.inputs]
        chunk_shapes = [
            t.chunk_shape if isinstance(t, TENSOR_TYPE) else t for t in inputs
        out_chunk_shape = broadcast_shape(*chunk_shapes)
        output = op.outputs[0]

        out_chunks = []
        nsplits = [[np.nan] * shape for shape in out_chunk_shape]
        get_index = lambda idx, t: tuple(
            0 if t.nsplits[i] == (1,) else ix for i, ix in enumerate(idx)
        for out_index in itertools.product(*(map(range, out_chunk_shape))):
            in_chunks = [
                t.cix[get_index(out_index[-t.ndim :], t)]
                if t.ndim != 0
                else t.chunks[0]
                for t in inputs
            chunk_shape = broadcast_shape(*(c.shape for c in in_chunks))
            out_chunk = (
                    in_chunks, shape=chunk_shape, index=out_index, order=output.order
            for i, idx, s in zip(itertools.count(0), out_index, out_chunk.shape):
                nsplits[i][idx] = s

        new_op = op.copy()
        return new_op.new_tensors(
            inputs, output.shape, order=output.order, chunks=out_chunks, nsplits=nsplits

    def execute(cls, ctx, op):
        (cond, x, y), device_id, xp = as_same_device(
            [ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True

        with device(device_id):
            ctx[op.outputs[0].key] = xp.where(cond, x, y)

[docs]def where(condition, x=None, y=None): """ Return elements, either from `x` or `y`, depending on `condition`. If only `condition` is given, return ``condition.nonzero()``. Parameters ---------- condition : array_like, bool When True, yield `x`, otherwise yield `y`. x, y : array_like, optional Values from which to choose. `x`, `y` and `condition` need to be broadcastable to some shape. Returns ------- out : Tensor or tuple of Tensors If both `x` and `y` are specified, the output tensor contains elements of `x` where `condition` is True, and elements from `y` elsewhere. If only `condition` is given, return the tuple ``condition.nonzero()``, the indices where `condition` is True. See Also -------- nonzero, choose Notes ----- If `x` and `y` are given and input arrays are 1-D, `where` is equivalent to:: [xv if c else yv for (c,xv,yv) in zip(condition,x,y)] Examples -------- >>> import mars.tensor as mt >>> mt.where([[True, False], [True, True]], ... [[1, 2], [3, 4]], ... [[9, 8], [7, 6]]).execute() array([[1, 8], [3, 4]]) >>> mt.where([[0, 1], [1, 0]]).execute() (array([0, 1]), array([1, 0])) >>> x = mt.arange(9.).reshape(3, 3) >>> mt.where( x > 5 ).execute() (array([2, 2, 2]), array([0, 1, 2])) >>> mt.where(x < 5, x, -1).execute() # Note: broadcasting. array([[ 0., 1., 2.], [ 3., 4., -1.], [-1., -1., -1.]]) Find the indices of elements of `x` that are in `goodvalues`. >>> goodvalues = [3, 4, 7] >>> ix = mt.isin(x, goodvalues) >>> ix.execute() array([[False, False, False], [ True, True, False], [False, True, False]]) >>> mt.where(ix).execute() (array([1, 1, 2]), array([0, 1, 1])) """ if (x is None) != (y is None): raise ValueError("either both or neither of x and y should be given") if x is None and y is None: return astensor(condition).nonzero() x, y = astensor(x), astensor(y) dtype = np.result_type(x.dtype, y.dtype) shape = broadcast_shape(x.shape, y.shape) if np.isscalar(condition): return broadcast_to(x if condition else y, shape).astype(dtype) else: condition = astensor(condition) op = TensorWhere(dtype=dtype) return op(condition, x, y, shape=shape)