Source code for hail.linalg.blockmatrix

import hail as hl
from hail.utils import new_temp_file, new_local_temp_file, local_path_uri, storage_level
from hail.utils.java import Env, jarray, joption
from hail.typecheck import *
from hail.table import Table
from hail.expr.expressions import expr_float64, matrix_table_source, check_entry_indexed
import numpy as np
import itertools
from enum import IntEnum

block_matrix_type = lazy()


class Form(IntEnum):
    SCALAR = 0
    COLUMN = 1
    ROW = 2
    MATRIX = 3

    @classmethod
    def of(cls, shape):
        assert len(shape) == 2
        if shape[0] == 1 and shape[1] == 1:
            return Form.SCALAR
        elif shape[1] == 1:
            return Form.COLUMN
        elif shape[0] == 1:
            return Form.ROW
        else:
            return Form.MATRIX

    @staticmethod
    def compatible(shape_a, shape_b, op):
        form_a = Form.of(shape_a)
        form_b = Form.of(shape_b)
        if (form_a == Form.SCALAR or
                form_b == Form.SCALAR or
                form_a == form_b and shape_a == shape_b or
                {form_a, form_b} == {Form.MATRIX, Form.COLUMN} and shape_a[0] == shape_b[0] or
                {form_a, form_b} == {Form.MATRIX, Form.ROW} and shape_a[1] == shape_b[1]):
            return form_a, form_b
        else:
            raise ValueError(f'incompatible shapes for {op}: {shape_a} and {shape_b}')


[docs]class BlockMatrix(object): """Hail's block-distributed matrix of :py:data:`.tfloat64` elements. .. include:: ../_templates/experimental.rst A block matrix is a distributed analogue of a two-dimensional `NumPy ndarray <https://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html>`__ with shape ``(n_rows, n_cols)`` and NumPy dtype ``float64``. Import the class with: >>> from hail.linalg import BlockMatrix Under the hood, block matrices are partitioned like a checkerboard into square blocks with side length a common block size. Blocks in the final row or column of blocks may be truncated, so block size need not evenly divide the matrix dimensions. Block size defaults to the value given by :meth:`default_block_size`. **Operations and broadcasting** The core operations are consistent with NumPy: ``+``, ``-``, ``*``, and ``/`` for element-wise addition, subtraction, multiplication, and division; ``@`` for matrix multiplication; ``T`` for transpose; and ``**`` for element-wise exponentiation to a scalar power. For element-wise binary operations, each operand may be a block matrix, an ndarray, or a scalar (:obj:`int` or :obj:`float`). For matrix multiplication, each operand may be a block matrix or an ndarray. If either operand is a block matrix, the result is a block matrix. Binary operations between block matrices require that both operands have the same block size. To interoperate with block matrices, ndarray operands must be one or two dimensional with dtype convertible to ``float64``. One-dimensional ndarrays of shape ``(n)`` are promoted to two-dimensional ndarrays of shape ``(1, n)``, i.e. a single row. Block matrices support broadcasting of ``+``, ``-``, ``*``, and ``/`` between matrices of different shapes, consistent with the NumPy `broadcasting rules <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`__. There is one exception: block matrices do not currently support element-wise "outer product" of a single row and a single column, although the same effect can be achieved for ``*`` by using ``@``. Warning ------- For binary operations, if the first operand is an ndarray and the second operand is a block matrix, the result will be a ndarray of block matrices. To achieve the desired behavior for ``+`` and ``*``, place the block matrix operand first; for ``-``, ``/``, and ``@``, first convert the ndarray to a block matrix using :meth:`.from_numpy`. **Indexing and slicing** Block matrices also support NumPy-style 2-dimensional `indexing and slicing <https://docs.scipy.org/doc/numpy/user/basics.indexing.html>`__, with two differences. First, slices ``start:stop:step`` must be non-empty with positive ``step``. Second, even if only one index is a slice, the resulting block matrix is still 2-dimensional. For example, for a block matrix ``bm`` with 10 rows and 10 columns: - ``bm[0, 0]`` is the element in row 0 and column 0 of ``bm``. - ``bm[0:1, 0]`` is a block matrix with 1 row, 1 column, and element ``bm[0, 0]``. - ``bm[2, :]`` is a block matrix with 1 row, 10 columns, and elements from row 2 of ``bm``. - ``bm[:3, -1]`` is a block matrix with 3 rows, 1 column, and the first 3 elements of the last column of ``bm``. - ``bm[::2, ::2]`` is a block matrix with 5 rows, 5 columns, and all evenly-indexed elements of ``bm``. Use :meth:`filter`, :meth:`filter_rows`, and :meth:`filter_cols` to subset to non-slice subsets of rows and columns, e.g. to rows ``[0, 2, 5]``. **Block-sparse representation** By default, block matrices compute and store all blocks explicitly. However, some applications involve block matrices in which: - some blocks consist entirely of zeroes. - some blocks are not of interest. For example, statistical geneticists often want to compute and manipulate a banded correlation matrix capturing "linkage disequilibrium" between nearby variants along the genome. In this case, working with the full correlation matrix for tens of millions of variants would be prohibitively expensive, and in any case, entries far from the diagonal are either not of interest or ought to be zeroed out before downstream linear algebra. To enable such computations, block matrices do not require that all blocks be realized explicitly. Implicit (dropped) blocks behave as blocks of zeroes, so we refer to a block matrix in which at least one block is implicitly zero as a **block-sparse matrix**. Otherwise, we say the matrix is block-dense. The property :meth:`is_sparse` encodes this state. Dropped blocks are not stored in memory or on :meth:`write`. In fact, blocks that are dropped prior to an action like :meth:`export` or :meth:`to_numpy` are never computed in the first place, nor are any blocks of upstream operands on which only dropped blocks depend! In addition, linear algebra is accelerated by avoiding, for example, explicit addition of or multiplication by blocks of zeroes. Block-sparse matrices may be created with :meth:`sparsify_band`, :meth:`sparsify_rectangles`, :meth:`sparsify_row_intervals`, and :meth:`sparsify_triangle`. The following methods naturally propagate block-sparsity: - Addition and subtraction "union" realized blocks. - Element-wise multiplication "intersects" realized blocks. - Transpose "transposes" realized blocks. - :meth:`abs` and :meth:`sqrt` preserve the realized blocks. - :meth:`sum` along an axis realizes those blocks for which at least one block summand is realized. These following methods always result in a block-dense matrix: - :meth:`fill` - Addition or subtraction of a scalar or broadcasted vector. - Matrix multiplication, ``@``. - Matrix slicing, and more generally :meth:`filter`, :meth:`filter_rows`, and :meth:`filter_cols`. The following methods fail if any operand is block-sparse, but can be forced by first applying :meth:`densify`. - Element-wise division between two block matrices. - Multiplication by a scalar or broadcasted vector which includes an infinite or ``nan`` value. - Division by a scalar or broadcasted vector which includes a zero, infinite or ``nan`` value. - Division of a scalar or broadcasted vector by a block matrix. - Element-wise exponentiation by a negative exponent. - Natural logarithm, :meth:`log`. """ def __init__(self, jbm): self._jbm = jbm
[docs] @classmethod @typecheck_method(path=str) def read(cls, path): """Reads a block matrix. Parameters ---------- path: :obj:`str` Path to input file. Returns ------- :class:`.BlockMatrix` """
return cls(Env.hail().linalg.BlockMatrix.read(Env.hc()._jhc, path))
[docs] @classmethod @typecheck_method(uri=str, n_rows=int, n_cols=int, block_size=nullable(int)) def fromfile(cls, uri, n_rows, n_cols, block_size=None): """Creates a block matrix from a binary file. Examples -------- >>> import numpy as np >>> a = np.random.rand(10, 20) >>> a.tofile('/local/file') # doctest: +SKIP To create a block matrix of the same dimensions: >>> bm = BlockMatrix.fromfile('file:///local/file', 10, 20) # doctest: +SKIP Notes ----- This method, analogous to `numpy.fromfile <https://docs.scipy.org/doc/numpy/reference/generated/numpy.fromfile.html>`__, reads a binary file of float64 values in row-major order, such as that produced by `numpy.tofile <https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.tofile.html>`__ or :meth:`BlockMatrix.tofile`. Binary files produced and consumed by :meth:`.tofile` and :meth:`.fromfile` are not platform independent, so should only be used for inter-operating with NumPy, not storage. Use :meth:`BlockMatrix.write` and :meth:`BlockMatrix.read` to save and load block matrices, since these methods write and read blocks in parallel and are platform independent. A NumPy ndarray must have type float64 for the output of func:`numpy.tofile` to be a valid binary input to :meth:`.fromfile`. This is not checked. The number of entries must be less than :math:`2^{31}`. Parameters ---------- uri: :obj:`str`, optional URI of binary input file. n_rows: :obj:`int` Number of rows. n_cols: :obj:`int` Number of columns. block_size: :obj:`int`, optional Block size. Default given by :meth:`default_block_size`. See Also -------- :meth:`.from_numpy` """ if not block_size: block_size = BlockMatrix.default_block_size() return cls(Env.hail().linalg.BlockMatrix.fromBreezeMatrix( Env.hc()._jsc, _breeze_fromfile(uri, n_rows, n_cols),
block_size))
[docs] @classmethod @typecheck_method(ndarray=np.ndarray, block_size=nullable(int)) def from_numpy(cls, ndarray, block_size=None): """Distributes a `NumPy ndarray <https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html>`__ as a block matrix. Examples -------- >>> import numpy as np >>> a = np.random.rand(10, 20) >>> bm = BlockMatrix.from_numpy(a) Notes ----- The ndarray must have two dimensions, each of non-zero size. The number of entries must be less than :math:`2^{31}`. Parameters ---------- ndarray: :class:`numpy.ndarray` ndarray with two dimensions, each of non-zero size. block_size: :obj:`int`, optional Block size. Default given by :meth:`default_block_size`. Returns ------- :class:`.BlockMatrix` """ if not block_size: block_size = BlockMatrix.default_block_size() if any(i == 0 for i in ndarray.shape): raise ValueError(f'from_numpy: ndarray dimensions must be non-zero, found shape {ndarray.shape}') nd = _ndarray_as_2d(ndarray) nd = _ndarray_as_float64(nd) n_rows, n_cols = nd.shape path = new_local_temp_file() uri = local_path_uri(path) nd.tofile(path)
return cls.fromfile(uri, n_rows, n_cols, block_size)
[docs] @classmethod @typecheck_method(entry_expr=expr_float64, block_size=nullable(int)) def from_entry_expr(cls, entry_expr, block_size=None): """Creates a block matrix using a matrix table entry expression. Examples -------- >>> mt = hl.balding_nichols_model(3, 25, 50) >>> bm = BlockMatrix.from_entry_expr(mt.GT.n_alt_alleles()) Notes ----- Do not use this method if you want to store a copy of the resulting block matrix. Instead, use :meth:`write_from_entry_expr` followed by :meth:`BlockMatrix.read`. If a pipelined transformation significantly downsamples the rows of the underlying matrix table, then repartitioning the matrix table ahead of this method will greatly improve its performance. The method will fail if any values are missing. To be clear, special float values like ``nan`` are not missing values. Parameters ---------- entry_expr: :class:`.Float64Expression` Entry expression for numeric matrix entries. block_size: :obj:`int`, optional Block size. Default given by :meth:`.BlockMatrix.default_block_size`. Returns ------- :class:`.BlockMatrix` """ path = new_temp_file() cls.write_from_entry_expr(entry_expr, path, block_size)
return cls.read(path)
[docs] @classmethod @typecheck_method(n_rows=int, n_cols=int, block_size=nullable(int), seed=int, uniform=bool) def random(cls, n_rows, n_cols, block_size=None, seed=0, uniform=False): """Creates a block matrix with standard normal or uniform random entries. Examples -------- Create a block matrix with 10 rows, 20 columns, and standard normal entries: >>> bm = BlockMatrix.random(10, 20) Parameters ---------- n_rows: :obj:`int` Number of rows. n_cols: :obj:`int` Number of columns. block_size: :obj:`int`, optional Block size. Default given by :meth:`default_block_size`. seed: :obj:`int` Random seed. uniform: :obj:`bool` If ``True``, entries are drawn from the uniform distribution on [0,1]. If ``False``, entries are drawn from the standard normal distribution. Returns ------- :class:`.BlockMatrix` """ if not block_size: block_size = BlockMatrix.default_block_size()
return cls(Env.hail().linalg.BlockMatrix.random(Env.hc()._jhc, n_rows, n_cols, block_size, seed, uniform))
[docs] @classmethod @typecheck_method(n_rows=int, n_cols=int, value=float, block_size=nullable(int)) def fill(cls, n_rows, n_cols, value, block_size=None): """Creates a block matrix with all elements the same value. Examples -------- Create a block matrix with 10 rows, 20 columns, and all elements equal to ``1.0``: >>> bm = BlockMatrix.fill(10, 20, 1.0) Parameters ---------- n_rows: :obj:`int` Number of rows. n_cols: :obj:`int` Number of columns. value: :obj:`float` Value of all elements. block_size: :obj:`int`, optional Block size. Default given by :meth:`default_block_size`. Returns ------- :class:`.BlockMatrix` """ if not block_size: block_size = BlockMatrix.default_block_size()
return cls(Env.hail().linalg.BlockMatrix.fill(Env.hc()._jhc, n_rows, n_cols, value, block_size)) @classmethod @typecheck_method(n_rows=int, n_cols=int, data=sequenceof(float), row_major=bool, block_size=int) def _create(cls, n_rows, n_cols, data, row_major, block_size): """Private method for creating small test matrices.""" bdm = Env.hail().utils.richUtils.RichDenseMatrixDouble.apply(n_rows, n_cols, jarray(Env.jvm().double, data), row_major) return cls(Env.hail().linalg.BlockMatrix.fromBreezeMatrix(Env.hc()._jsc, bdm, block_size))
[docs] @staticmethod def default_block_size(): """Default block side length."""
return Env.hail().linalg.BlockMatrix.defaultBlockSize() @property def n_rows(self): """Number of rows. Returns ------- :obj:`int` """ return self._jbm.nRows() @property def n_cols(self): """Number of columns. Returns ------- :obj:`int` """ return self._jbm.nCols() @property def shape(self): """Shape of matrix. Returns ------- (:obj:`int`, :obj:`int`) Number of rows and number of columns. """ return self.n_rows, self.n_cols @property def block_size(self): """Block size. Returns ------- :obj:`int` """ return self._jbm.blockSize() @property def _jdata(self): return self._jbm.toBreezeMatrix().data() @property def _as_scalar(self): assert self.n_rows == 1 and self.n_cols == 1 return self._jbm.toBreezeMatrix().apply(0, 0)
[docs] @typecheck_method(path=str, force_row_major=bool, stage_locally=bool) def write(self, path, force_row_major=False, stage_locally=False): """Writes the block matrix. Parameters ---------- path: :obj:`str` Path for output file. force_row_major: :obj:`bool` If ``True``, transform blocks in column-major format to row-major format before writing. If ``False``, write blocks in their current format. stage_locally: :obj:`bool` If ``True``, major output will be written to temporary local storage before being copied to ``output``. """
self._jbm.write(path, force_row_major, stage_locally)
[docs] @staticmethod @typecheck(entry_expr=expr_float64, path=str, block_size=nullable(int)) def write_from_entry_expr(entry_expr, path, block_size=None): """Writes a block matrix from a matrix table entry expression. Examples -------- >>> mt = hl.balding_nichols_model(3, 25, 50) >>> BlockMatrix.write_from_entry_expr(mt.GT.n_alt_alleles(), ... 'output/model.bm') Notes ----- The resulting file can be loaded with :meth:`BlockMatrix.read`. Blocks are stored row-major. If a pipelined transformation significantly downsamples the rows of the underlying matrix table, then repartitioning the matrix table ahead of this method will greatly improve its performance. The method will fail if any values are missing. To be clear, special float values like ``nan`` are not missing values. Parameters ---------- entry_expr: :class:`.Float64Expression` Entry expression for numeric matrix entries. path: :obj:`str` Path for output. block_size: :obj:`int`, optional Block size. Default given by :meth:`.BlockMatrix.default_block_size`. """ if not block_size: block_size = BlockMatrix.default_block_size() check_entry_indexed('BlockMatrix.write_from_entry_expr', entry_expr) mt = matrix_table_source('BlockMatrix.write_from_entry_expr', entry_expr) # FIXME: remove once select_entries on a field is free if entry_expr in mt._fields_inverse: field = mt._fields_inverse[entry_expr] mt._jvds.writeBlockMatrix(path, field, block_size) else: field = Env.get_uid()
mt.select_entries(**{field: entry_expr})._jvds.writeBlockMatrix(path, field, block_size) @staticmethod def _check_indices(indices, size): if len(indices) == 0: raise ValueError('index list must be non-empty') elif not all(x < y for x, y in zip(indices, indices[1:])): raise ValueError('index list must be strictly increasing') elif indices[0] < 0: raise ValueError(f'index list values must be in range [0, {size}), found {indices[0]}') elif indices[-1] >= size: raise ValueError(f'index list values must be in range [0, {size}), found {indices[-1]}')
[docs] @typecheck_method(rows_to_keep=sequenceof(int)) def filter_rows(self, rows_to_keep): """Filters matrix rows. Parameters ---------- rows_to_keep: :obj:`list` of :obj:`int` Indices of rows to keep. Must be non-empty and increasing. Returns ------- :class:`.BlockMatrix` """ BlockMatrix._check_indices(rows_to_keep, self.n_rows)
return BlockMatrix(self._jbm.filterRows(jarray(Env.jvm().long, rows_to_keep)))
[docs] @typecheck_method(cols_to_keep=sequenceof(int)) def filter_cols(self, cols_to_keep): """Filters matrix columns. Parameters ---------- cols_to_keep: :obj:`list` of :obj:`int` Indices of columns to keep. Must be non-empty and increasing. Returns ------- :class:`.BlockMatrix` """ BlockMatrix._check_indices(cols_to_keep, self.n_cols)
return BlockMatrix(self._jbm.filterCols(jarray(Env.jvm().long, cols_to_keep)))
[docs] @typecheck_method(rows_to_keep=sequenceof(int), cols_to_keep=sequenceof(int)) def filter(self, rows_to_keep, cols_to_keep): """Filters matrix rows and columns. Notes ----- This method has the same effect as :meth:`BlockMatrix.filter_cols` followed by :meth:`BlockMatrix.filter_rows` (or vice versa), but filters the block matrix in a single pass which may be more efficient. Parameters ---------- rows_to_keep: :obj:`list` of :obj:`int` Indices of rows to keep. Must be non-empty and increasing. cols_to_keep: :obj:`list` of :obj:`int` Indices of columns to keep. Must be non-empty and increasing. Returns ------- :class:`.BlockMatrix` """ BlockMatrix._check_indices(rows_to_keep, self.n_rows) BlockMatrix._check_indices(cols_to_keep, self.n_cols) return BlockMatrix(self._jbm.filter(jarray(Env.jvm().long, rows_to_keep),
jarray(Env.jvm().long, cols_to_keep))) @staticmethod def _pos_index(i, size, name, allow_size=False): if 0 <= i < size or (i == size and allow_size): return i elif 0 <= i + size < size: return i + size else: raise ValueError(f'invalid {name} {i} for axis of size {size}') @staticmethod def _range_to_keep(idx, size): if isinstance(idx, int): i = BlockMatrix._pos_index(idx, size, 'index') return [i] elif isinstance(idx, slice): if idx.step and idx.step <= 0: raise ValueError(f'slice step must be positive, found {idx.step}') start = 0 if idx.start is None else BlockMatrix._pos_index(idx.start, size, 'start index') stop = size if idx.stop is None else BlockMatrix._pos_index(idx.stop, size, 'stop index', allow_size=True) step = 1 if idx.step is None else idx.step if start < stop: if 0 == start and stop == size and step == 1: return None else: return range(start, stop, step) else: raise ValueError(f'slice {start}:{stop}:{step} is empty') @typecheck_method(indices=tupleof(oneof(int, slice))) def __getitem__(self, indices): if len(indices) != 2: raise ValueError(f'tuple of indices or slices must have length two, found {len(indices)}') row_idx, col_idx = indices if isinstance(row_idx, int) and isinstance(col_idx, int): i = BlockMatrix._pos_index(row_idx, self.n_rows, 'row index') j = BlockMatrix._pos_index(col_idx, self.n_cols, 'col index') return self._jbm.getElement(i, j) rows_to_keep = BlockMatrix._range_to_keep(row_idx, self.n_rows) cols_to_keep = BlockMatrix._range_to_keep(col_idx, self.n_cols) if rows_to_keep is None and cols_to_keep is None: return self elif rows_to_keep is None and cols_to_keep is not None: return self.filter_cols(cols_to_keep) elif rows_to_keep is not None and cols_to_keep is None: return self.filter_rows(rows_to_keep) else: return self.filter(rows_to_keep, cols_to_keep) @typecheck_method(table=Table, radius=int, include_diagonal=bool) def _filtered_entries_table(self, table, radius, include_diagonal): return Table(self._jbm.filteredEntriesTable(table._jt, radius, include_diagonal))
[docs] @typecheck_method(lower=int, upper=int, blocks_only=bool) def sparsify_band(self, lower=0, upper=0, blocks_only=False): r"""Filter to a diagonal band. Examples -------- Consider the following block matrix: >>> import numpy as np >>> nd = np.array([[ 1.0, 2.0, 3.0, 4.0], ... [ 5.0, 6.0, 7.0, 8.0], ... [ 9.0, 10.0, 11.0, 12.0], ... [13.0, 14.0, 15.0, 16.0]]) >>> bm = BlockMatrix.from_numpy(nd, block_size=2) Filter to a band from one below the diagonal to two above the diagonal and collect to NumPy: >>> bm.sparsify_band(lower=-1, upper=2).to_numpy() array([[ 1., 2., 3., 0.], [ 5., 6., 7., 8.], [ 0., 10., 11., 12.], [ 0., 0., 15., 16.]]) Set all blocks fully outside the diagonal to zero and collect to NumPy: >>> bm.sparsify_band(lower=0, upper=0, blocks_only=True).to_numpy() array([[ 1., 2., 0., 0.], [ 5., 6., 0., 0.], [ 0., 0., 11., 12.], [ 0., 0., 15., 16.]]) Notes ----- This method creates a block-sparse matrix by zeroing out all blocks which are disjoint from a diagonal band. By default, all elements outside the band but inside blocks that overlap the band are set to zero as well. The band is defined in terms of inclusive `lower` and `upper` indices relative to the diagonal. For example, the indices -1, 0, and 1 correspond to the sub-diagonal, diagonal, and super-diagonal, respectively. The diagonal band contains the elements at positions :math:`(i, j)` such that .. math:: \mathrm{lower} \leq j - i \leq \mathrm{upper}. `lower` must be less than or equal to `upper`, but their values may exceed the dimensions of the matrix, the band need not include the diagonal, and the matrix need not be square. Parameters ---------- lower: :obj:`int` Index of lowest band relative to the diagonal. upper: :obj:`int` Index of highest band relative to the diagonal. blocks_only: :obj:`bool` If ``False``, set all elements outside the band to zero. If ``True``, only set all blocks outside the band to blocks of zeros; this is more efficient. Returns ------- :class:`.BlockMatrix` Sparse block matrix. """ if lower > upper: raise ValueError(f'sparsify_band: lower={lower} is greater than upper={upper}')
return BlockMatrix(self._jbm.filterBand(lower, upper, blocks_only))
[docs] @typecheck_method(lower=bool, blocks_only=bool) def sparsify_triangle(self, lower=False, blocks_only=False): """Filter to the upper or lower triangle. Examples -------- Consider the following block matrix: >>> import numpy as np >>> nd = np.array([[ 1.0, 2.0, 3.0, 4.0], ... [ 5.0, 6.0, 7.0, 8.0], ... [ 9.0, 10.0, 11.0, 12.0], ... [13.0, 14.0, 15.0, 16.0]]) >>> bm = BlockMatrix.from_numpy(nd, block_size=2) Filter to the upper triangle and collect to NumPy: >>> bm.sparsify_triangle().to_numpy() array([[ 1., 2., 3., 4.], [ 0., 6., 7., 8.], [ 0., 0., 11., 12.], [ 0., 0., 0., 16.]]) Set all blocks fully outside the upper triangle to zero and collect to NumPy: >>> bm.sparsify_triangle(blocks_only=True).to_numpy() array([[ 1., 2., 3., 4.], [ 5., 6., 7., 8.], [ 0., 0., 11., 12.], [ 0., 0., 15., 16.]]) Notes ----- This method creates a block-sparse matrix by zeroing out all blocks which are disjoint from the (non-strict) upper or lower triangle. By default, all elements outside the triangle but inside blocks that overlap the triangle are set to zero as well. Parameters ---------- lower: :obj:`bool` If ``False``, keep the upper triangle. If ``True``, keep the lower triangle. blocks_only: :obj:`bool` If ``False``, set all elements outside the triangle to zero. If ``True``, only set all blocks outside the triangle to blocks of zeros; this is more efficient. Returns ------- :class:`.BlockMatrix` Sparse block matrix. """ if lower: lower_band = 1 - self.n_rows upper_band = 0 else: lower_band = 0 upper_band = self.n_cols - 1
return self.sparsify_band(lower_band, upper_band, blocks_only)
[docs] @typecheck_method(starts=oneof(sequenceof(int), np.ndarray), stops=oneof(sequenceof(int), np.ndarray), blocks_only=bool) def sparsify_row_intervals(self, starts, stops, blocks_only=False): """Creates a block-sparse matrix by filtering to an interval for each row. Examples -------- Consider the following block matrix: >>> import numpy as np >>> nd = np.array([[ 1.0, 2.0, 3.0, 4.0], ... [ 5.0, 6.0, 7.0, 8.0], ... [ 9.0, 10.0, 11.0, 12.0], ... [13.0, 14.0, 15.0, 16.0]]) >>> bm = BlockMatrix.from_numpy(nd, block_size=2) Set all elements outside the given row intervals to zero and collect to NumPy: >>> (bm.sparsify_row_intervals(starts=[1, 0, 2, 2], ... stops= [2, 0, 3, 4]) ... .to_numpy()) array([[ 0., 2., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 11., 0.], [ 0., 0., 15., 16.]]) Set all blocks fully outside the given row intervals to blocks of zeros and collect to NumPy: >>> (bm.sparsify_row_intervals(starts=[1, 0, 2, 2], ... stops= [2, 0, 3, 4], ... blocks_only=True) ... .to_numpy()) array([[ 1., 2., 0., 0.], [ 5., 6., 0., 0.], [ 0., 0., 11., 12.], [ 0., 0., 15., 16.]]) Notes ----- This method creates a block-sparse matrix by zeroing out all blocks which are disjoint from all row intervals. By default, all elements outside the row intervals but inside blocks that overlap the row intervals are set to zero as well. `starts` and `stops` must both have length equal to the number of rows. The interval for row ``i`` is ``[starts[i], stops[i])``. In particular, ``0 <= starts[i] <= stops[i] <= n_cols`` is required for all ``i``. This method requires the number of rows to be less than :math:`2^{31}`. Parameters ---------- starts: :obj:`list` of :obj:`int`, or :class:`ndarray` of :obj:`int32` or :obj:`int64` Start indices for each row (inclusive). stops: :obj:`list` of :obj:`int`, or :class:`ndarray` of :obj:`int32` or :obj:`int64` Stop indices for each row (exclusive). blocks_only: :obj:`bool` If ``False``, set all elements outside row intervals to zero. If ``True``, only set all blocks outside row intervals to blocks of zeros; this is more efficient. Returns ------- :class:`.BlockMatrix` Sparse block matrix. """ if isinstance(starts, np.ndarray): if not (starts.dtype == np.int32 or starts.dtype == np.int64): raise ValueError("sparsify_row_intervals: starts ndarray must have dtype 'int32' or 'int64'") starts = [int(s) for s in starts] if isinstance(stops, np.ndarray): if not (stops.dtype == np.int32 or stops.dtype == np.int64): raise ValueError("sparsify_row_intervals: stops ndarray must have dtype 'int32' or 'int64'") stops = [int(s) for s in stops] n_rows = self.n_rows n_cols = self.n_cols if n_rows >= (1 << 31): raise ValueError(f'n_rows must be less than 2^31, found {n_rows}') if len(starts) != n_rows or len(stops) != n_rows: raise ValueError(f'starts and stops must both have length {n_rows} (the number of rows)') if any([start < 0 for start in starts]): raise ValueError('all start values must be non-negative') if any([stop > self.n_cols for stop in stops]): raise ValueError(f'all stop values must be less than or equal to {n_cols} (the number of columns)') if any([starts[i] > stops[i] for i in range(0, n_rows)]): raise ValueError('every start value must be less than or equal to the corresponding stop value') return BlockMatrix(self._jbm.filterRowIntervals( jarray(Env.jvm().long, starts), jarray(Env.jvm().long, stops),
blocks_only))
[docs] @typecheck_method(uri=str) def tofile(self, uri): """Collects and writes data to a binary file. Examples -------- >>> import numpy as np >>> bm = BlockMatrix.random(10, 20) >>> bm.tofile('file:///local/file') # doctest: +SKIP To create a :class:`numpy.ndarray` of the same dimensions: >>> a = np.fromfile('/local/file').reshape((10, 20)) # doctest: +SKIP Notes ----- This method, analogous to `numpy.tofile <https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.tofile.html>`__, produces a binary file of float64 values in row-major order, which can be read by functions such as `numpy.fromfile <https://docs.scipy.org/doc/numpy/reference/generated/numpy.fromfile.html>`__ (if a local file) and :meth:`BlockMatrix.fromfile`. Binary files produced and consumed by :meth:`.tofile` and :meth:`.fromfile` are not platform independent, so should only be used for inter-operating with NumPy, not storage. Use :meth:`BlockMatrix.write` and :meth:`BlockMatrix.read` to save and load block matrices, since these methods write and read blocks in parallel and are platform independent. The number of entries must be less than :math:`2^{31}`. Parameters ---------- uri: :obj:`str`, optional URI of binary output file. See Also -------- :meth:`.to_numpy` """ n_entries = self.n_rows * self.n_cols if n_entries >= 2 << 31: raise ValueError(f'number of entries must be less than 2^31, found {n_entries}') bdm = self._jbm.toBreezeMatrix() row_major = Env.hail().utils.richUtils.RichDenseMatrixDouble.exportToDoubles(Env.hc()._jhc, uri, bdm, True)
assert row_major
[docs] def to_numpy(self): """Collects the block matrix into a `NumPy ndarray <https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html>`__. Examples -------- >>> bm = BlockMatrix.random(10, 20) >>> a = bm.to_numpy() Notes ----- The number of entries must be less than :math:`2^{31}`. The resulting ndarray will have the same shape as the block matrix. Returns ------- :class:`numpy.ndarray` """ path = new_local_temp_file() uri = local_path_uri(path) self.tofile(uri)
return np.fromfile(path).reshape((self.n_rows, self.n_cols)) @property def is_sparse(self): """Returns ``True`` if block-sparse. Notes ----- A block matrix is block-sparse if at least of its blocks is dropped, i.e. implicitly a block of zeros. Returns ------- :obj:`bool` """ return self._jbm.gp().maybeBlocks().isDefined() @property def T(self): """Matrix transpose. Returns ------- :class:`.BlockMatrix` """ return BlockMatrix(self._jbm.transpose())
[docs] def densify(self): """Restore all dropped blocks as explicit blocks of zeros. Returns ------- :class:`.BlockMatrix` """
return BlockMatrix(self._jbm.densify())
[docs] def cache(self): """Persist this block matrix in memory. Notes ----- This method is an alias for :meth:`persist("MEMORY_ONLY") <hail.linalg.BlockMatrix.persist>`. Returns ------- :class:`.BlockMatrix` Cached block matrix. """
return self.persist('MEMORY_ONLY')
[docs] @typecheck_method(storage_level=storage_level) def persist(self, storage_level='MEMORY_AND_DISK'): """Persists this block matrix in memory or on disk. Notes ----- The :meth:`.BlockMatrix.persist` and :meth:`.BlockMatrix.cache` methods store the current block matrix on disk or in memory temporarily to avoid redundant computation and improve the performance of Hail pipelines. This method is not a substitution for :meth:`.BlockMatrix.write`, which stores a permanent file. Most users should use the "MEMORY_AND_DISK" storage level. See the `Spark documentation <http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence>`__ for a more in-depth discussion of persisting data. Parameters ---------- storage_level : str Storage level. One of: NONE, DISK_ONLY, DISK_ONLY_2, MEMORY_ONLY, MEMORY_ONLY_2, MEMORY_ONLY_SER, MEMORY_ONLY_SER_2, MEMORY_AND_DISK, MEMORY_AND_DISK_2, MEMORY_AND_DISK_SER, MEMORY_AND_DISK_SER_2, OFF_HEAP Returns ------- :class:`.BlockMatrix` Persisted block matrix. """
return BlockMatrix(self._jbm.persist(storage_level))
[docs] def unpersist(self): """Unpersists this block matrix from memory/disk. Notes ----- This function will have no effect on a block matrix that was not previously persisted. Returns ------- :class:`.BlockMatrix` Unpersisted block matrix. """
return BlockMatrix(self._jbm.unpersist()) def __pos__(self): return self def __neg__(self): """Negation: -a. Returns ------- :class:`.BlockMatrix` """ op = getattr(self._jbm, "unary_$minus") return BlockMatrix(op()) def _promote(self, b, op, reverse=False): a = self form_a, form_b = Form.compatible(a.shape, _shape(b), op) if form_b > form_a: if isinstance(b, np.ndarray): b = BlockMatrix.from_numpy(b, a.block_size) return b._promote(a, op, reverse=True) assert form_a >= form_b if form_b == Form.SCALAR: if isinstance(b, int) or isinstance(b, float): b = float(b) elif isinstance(b, np.ndarray): b = _ndarray_as_float64(b).item() else: b = b._as_scalar elif form_a > form_b: if isinstance(b, np.ndarray): b = _jarray_from_ndarray(b) else: assert isinstance(b, BlockMatrix) b = b._jdata else: assert form_a == form_b if not isinstance(b, BlockMatrix): assert isinstance(b, np.ndarray) b = BlockMatrix.from_numpy(b, a.block_size) assert (isinstance(a, BlockMatrix) and (isinstance(b, BlockMatrix) or isinstance(b, float) or b.getClass().isArray()) and (not (isinstance(b, BlockMatrix) and reverse))) return a, b, form_b, reverse @typecheck_method(b=oneof(numeric, np.ndarray, block_matrix_type)) def __add__(self, b): """Addition: a + b. Parameters ---------- b: :obj:`int` or :obj:`float` or :class:`numpy.ndarray` or :class:`BlockMatrix` Returns ------- :class:`.BlockMatrix` """ new_a, new_b, form_b, _ = self._promote(b, 'addition') if isinstance(new_b, float): return BlockMatrix(new_a._jbm.scalarAdd(new_b)) elif isinstance(new_b, BlockMatrix): return BlockMatrix(new_a._jbm.add(new_b._jbm)) else: assert new_b.getClass().isArray() if form_b == Form.COLUMN: return BlockMatrix(new_a._jbm.colVectorAdd(new_b)) else: assert form_b == Form.ROW return BlockMatrix(new_a._jbm.rowVectorAdd(new_b)) @typecheck_method(b=oneof(numeric, np.ndarray, block_matrix_type)) def __sub__(self, b): """Subtraction: a - b. Parameters ---------- b: :obj:`int` or :obj:`float` or :class:`numpy.ndarray` or :class:`BlockMatrix` Returns ------- :class:`.BlockMatrix` """ new_a, new_b, form_b, reverse = self._promote(b, 'subtraction') if isinstance(new_b, float): if reverse: return BlockMatrix(new_a._jbm.reverseScalarSub(new_b)) else: return BlockMatrix(new_a._jbm.scalarSub(new_b)) elif isinstance(new_b, BlockMatrix): assert not reverse return BlockMatrix(new_a._jbm.sub(new_b._jbm)) else: assert new_b.getClass().isArray() if form_b == Form.COLUMN: if reverse: return BlockMatrix(new_a._jbm.reverseColVectorSub(new_b)) else: return BlockMatrix(new_a._jbm.colVectorSub(new_b)) else: assert form_b == Form.ROW if reverse: return BlockMatrix(new_a._jbm.reverseRowVectorSub(new_b)) else: return BlockMatrix(new_a._jbm.rowVectorSub(new_b)) @typecheck_method(b=oneof(numeric, np.ndarray, block_matrix_type)) def __mul__(self, b): """Element-wise multiplication: a * b. Parameters ---------- b: :obj:`int` or :obj:`float` or :class:`numpy.ndarray` or :class:`BlockMatrix` Returns ------- :class:`.BlockMatrix` """ new_a, new_b, form_b, _ = self._promote(b, 'element-wise multiplication') if isinstance(new_b, float): return BlockMatrix(new_a._jbm.scalarMul(new_b)) elif isinstance(new_b, BlockMatrix): return BlockMatrix(new_a._jbm.mul(new_b._jbm)) else: assert new_b.getClass().isArray() if form_b == Form.COLUMN: return BlockMatrix(new_a._jbm.colVectorMul(new_b)) else: assert form_b == Form.ROW return BlockMatrix(new_a._jbm.rowVectorMul(new_b)) @typecheck_method(b=oneof(numeric, np.ndarray, block_matrix_type)) def __truediv__(self, b): """Element-wise division: a / b. Parameters ---------- b: :obj:`int` or :obj:`float` or :class:`numpy.ndarray` or :class:`BlockMatrix` Returns ------- :class:`.BlockMatrix` """ new_a, new_b, form_b, reverse = self._promote(b, 'element-wise division') if isinstance(new_b, float): if reverse: return BlockMatrix(new_a._jbm.reverseScalarDiv(new_b)) else: return BlockMatrix(new_a._jbm.scalarDiv(new_b)) elif isinstance(new_b, BlockMatrix): assert not reverse return BlockMatrix(new_a._jbm.div(new_b._jbm)) else: assert new_b.getClass().isArray() if form_b == Form.COLUMN: if reverse: return BlockMatrix(new_a._jbm.reverseColVectorDiv(new_b)) else: return BlockMatrix(new_a._jbm.colVectorDiv(new_b)) else: assert form_b == Form.ROW if reverse: return BlockMatrix(new_a._jbm.reverseRowVectorDiv(new_b)) else: return BlockMatrix(new_a._jbm.rowVectorDiv(new_b)) @typecheck_method(b=numeric) def __radd__(self, b): return self + b @typecheck_method(b=numeric) def __rsub__(self, b): return BlockMatrix(self._jbm.reverseScalarSub(float(b))) @typecheck_method(b=numeric) def __rmul__(self, b): return self * b @typecheck_method(b=numeric) def __rtruediv__(self, b): return BlockMatrix(self._jbm.reverseScalarDiv(float(b))) @typecheck_method(b=oneof(np.ndarray, block_matrix_type)) def __matmul__(self, b): """Matrix multiplication: a @ b. Parameters ---------- b: :class:`numpy.ndarray` or :class:`BlockMatrix` Returns ------- :class:`.BlockMatrix` """ if isinstance(b, np.ndarray): return self @ BlockMatrix.from_numpy(b, self.block_size) else: if self.n_cols != b.n_rows: raise ValueError(f'incompatible shapes for matrix multiplication: {self.shape} and {b.shape}') return BlockMatrix(self._jbm.dot(b._jbm)) @typecheck_method(x=numeric) def __pow__(self, x): """Element-wise exponentiation: a ** x. Parameters ---------- x: :obj:`int` or :obj:`float` Exponent. Returns ------- :class:`.BlockMatrix` """ return BlockMatrix(self._jbm.pow(float(x)))
[docs] def sqrt(self): """Element-wise square root. Returns ------- :class:`.BlockMatrix` """
return BlockMatrix(self._jbm.sqrt())
[docs] def abs(self): """Element-wise absolute value. Returns ------- :class:`.BlockMatrix` """
return BlockMatrix(self._jbm.abs())
[docs] def log(self): """Element-wise natural logarithm. Returns ------- :class:`.BlockMatrix` """
return BlockMatrix(self._jbm.log())
[docs] def diagonal(self): """Extracts diagonal elements as ndarray. Returns ------- :class:`numpy.ndarray` """
return _ndarray_from_jarray(self._jbm.diagonal())
[docs] @typecheck_method(axis=nullable(int)) def sum(self, axis=None): """Sums array elements over one or both axes. Examples -------- >>> import numpy as np >>> nd = np.array([[ 1.0, 2.0, 3.0], ... [ 4.0, 5.0, 6.0]]) >>> bm = BlockMatrix.from_numpy(nd) >>> bm.sum() 21.0 >>> bm.sum(axis=0).to_numpy() array([[5., 7., 9.]]) >>> bm.sum(axis=1).to_numpy() array([[ 6.], [15.]]) Parameters ---------- axis: :obj:`int`, optional Axis over which to sum. By default, sum all elements. If ``0``, sum over rows. If ``1``, sum over columns. Returns ------- :obj:`float` or :class:`BlockMatrix` If None, returns a float. If ``0``, returns a block matrix with a single row. If ``1``, returns a block matrix with a single column. """ if axis is None: return self._jbm.sum() elif axis == 0: return BlockMatrix(self._jbm.rowSum()) elif axis == 1: return BlockMatrix(self._jbm.colSum()) else:
raise ValueError(f'axis must be None, 0, or 1: found {axis}')
[docs] def entries(self): """Returns a table with the indices and value of each block matrix entry. Examples -------- >>> import numpy as np >>> block_matrix = BlockMatrix.from_numpy(np.array([[5, 7], [2, 8]]), 2) >>> entries_table = block_matrix.entries() >>> entries_table.show() +-------+-------+-------------+ | i | j | entry | +-------+-------+-------------+ | int64 | int64 | float64 | +-------+-------+-------------+ | 0 | 0 | 5.00000e+00 | | 0 | 1 | 7.00000e+00 | | 1 | 0 | 2.00000e+00 | | 1 | 1 | 8.00000e+00 | +-------+-------+-------------+ Notes ----- The resulting table may be filtered, aggregated, and queried, but should only be directly exported to disk if the block matrix is very small. For block-sparse matrices, only realized blocks are included. To force inclusion of zeroes in dropped blocks, apply :meth:`densify` first. The resulting table has the following fields: - **i** (:py:data:`.tint64`, key field) -- Row index. - **j** (:py:data:`.tint64`, key field) -- Column index. - **entry** (:py:data:`.tfloat64`) -- Value of entry. Returns ------- :class:`.Table` Table with a row for each entry. """
return Table(self._jbm.entriesTable(Env.hc()._jhc))
[docs] @staticmethod @typecheck(path_in=str, path_out=str, delimiter=str, header=nullable(str), add_index=bool, parallel=nullable(enumeration('separate_header', 'header_per_shard')), partition_size=nullable(int), entries=enumeration('full', 'lower', 'strict_lower', 'upper', 'strict_upper')) def export(path_in, path_out, delimiter='\t', header=None, add_index=False, parallel=None, partition_size=None, entries='full'): """Exports a stored block matrix as a delimited text file. Examples -------- Consider the following matrix. >>> import numpy as np >>> nd = np.array([[1.0, 0.8, 0.7], ... [0.8, 1.0 ,0.3], ... [0.7, 0.3, 1.0]]) >>> BlockMatrix.from_numpy(nd).write('output/example.bm', force_row_major=True) Export the full matrix as a file with tab-separated values: >>> BlockMatrix.export('output/example.bm', 'output/example.tsv') Export the upper-triangle of the matrix as a block gzipped file of comma-separated values. >>> BlockMatrix.export(path_in='output/example.bm', ... path_out='output/example.csv.bgz', ... delimiter=',', ... entries='upper') Export the full matrix with row indices in parallel as a folder of gzipped files, each with a header line for columns ``idx``, ``A``, ``B``, and ``C``. >>> BlockMatrix.export(path_in='output/example.bm', ... path_out='output/example.gz', ... header='\t'.join(['idx', 'A', 'B', 'C']), ... add_index=True, ... parallel='header_per_shard', ... partition_size=2) This produces two compressed files which uncompress to: .. code-block:: text idx A B C 0 1.0 0.8 0.7 1 0.8 1.0 0.3 .. code-block:: text idx A B C 2 0.7 0.3 1.0 Warning ------- The block matrix must be stored in row-major format, as results from :meth:`.BlockMatrix.write` with ``force_row_major=True`` and from :meth:`.BlockMatrix.write_from_entry_expr`. Otherwise, :meth:`export` will fail. Notes ----- The five options for `entries` are illustrated below. Full: .. code-block:: text 1.0 0.8 0.7 0.8 1.0 0.3 0.7 0.3 1.0 Lower triangle: .. code-block:: text 1.0 0.8 1.0 0.7 0.3 1.0 Strict lower triangle: .. code-block:: text 0.8 0.7 0.3 Upper triangle: .. code-block:: text 1.0 0.8 0.7 1.0 0.3 1.0 Strict upper triangle: .. code-block:: text 0.8 0.7 0.3 The number of columns must be less than :math:`2^{31}`. The number of partitions (file shards) exported equals the ceiling of ``n_rows / partition_size``. By default, there is one partition per row of blocks in the block matrix. The number of partitions should be at least the number of cores for efficient parallelism. Setting the partition size to an exact (rather than approximate) divisor or multiple of the block size reduces superfluous shuffling of data. If `parallel` is ``None``, these file shards are then serially concatenated by one core into one file, a slow process. See other options below. It is highly recommended to export large files with a ``.bgz`` extension, which will use a block gzipped compression codec. These files can be read natively with Python's ``gzip.open`` and R's ``read.table``. Parameters ---------- path_in: :obj:`str` Path to input block matrix, stored row-major on disk. path_out: :obj:`str` Path for export. Use extension ``.gz`` for gzip or ``.bgz`` for block gzip. delimiter: :obj:`str` Column delimiter. header: :obj:`str`, optional If provided, `header` is prepended before the first row of data. add_index: :obj:`bool` If ``True``, add an initial column with the absolute row index. parallel: :obj:`str`, optional If ``'header_per_shard'``, create a folder with one file per partition, each with a header if provided. If ``'separate_header'``, create a folder with one file per partition without a header; write the header, if provided, in a separate file. If ``None``, serially concatenate the header and all partitions into one file; export will be slower. If `header` is ``None`` then ``'header_per_shard'`` and ``'separate_header'`` are equivalent. partition_size: :obj:`int`, optional Number of rows to group per partition for export. Default given by block size of the block matrix. entries: :obj:`str Describes which entries to export. One of: ``'full'``, ``'lower'``, ``'strict_lower'``, ``'upper'``, ``'strict_upper'``. """ jrm = Env.hail().linalg.RowMatrix.readBlockMatrix(Env.hc()._jhc, path_in, joption(partition_size)) export_type = Env.hail().utils.ExportType.getExportType(parallel) if entries == 'full': jrm.export(path_out, delimiter, joption(header), add_index, export_type) elif entries == 'lower': jrm.exportLowerTriangle(path_out, delimiter, joption(header), add_index, export_type) elif entries == 'strict_lower': jrm.exportStrictLowerTriangle(path_out, delimiter, joption(header), add_index, export_type) elif entries == 'upper': jrm.exportUpperTriangle(path_out, delimiter, joption(header), add_index, export_type) else: assert entries == 'strict_upper'
jrm.exportStrictUpperTriangle(path_out, delimiter, joption(header), add_index, export_type)
[docs] @typecheck_method(rectangles=sequenceof(sequenceof(int))) def sparsify_rectangles(self, rectangles): """Filter to blocks overlapping the union of rectangular regions. Examples -------- Consider the following block matrix: >>> import numpy as np >>> nd = np.array([[ 1.0, 2.0, 3.0, 4.0], ... [ 5.0, 6.0, 7.0, 8.0], ... [ 9.0, 10.0, 11.0, 12.0], ... [13.0, 14.0, 15.0, 16.0]]) >>> bm = BlockMatrix.from_numpy(nd) Filter to blocks covering three rectangles and collect to NumPy: >>> bm.sparsify_rectangles([[0, 1, 0, 1], [0, 3, 0, 2], [1, 2, 0, 4]]).to_numpy() array([[ 1., 2., 3., 4.], [ 5., 6., 7., 8.], [ 9., 10., 0., 0.], [13., 14., 0., 0.]]) Notes ----- This method creates a block-sparse matrix by zeroing out (dropping) all blocks which are disjoint from the union of a set of rectangular regions. Partially overlapping blocks are *not* modified. Each rectangle is encoded as a list of length four of the form ``[row_start, row_stop, col_start, col_stop]``, where starts are inclusive and stops are exclusive. These must satisfy ``0 <= row_start <= row_stop <= n_rows`` and ``0 <= col_start <= col_stop <= n_cols``. For example ``[0, 2, 1, 3]`` corresponds to the row-index range ``[0, 2)`` and column-index range ``[1, 3)``, i.e. the elements at positions ``(0, 1)``, ``(0, 2)``, ``(1, 1)``, and ``(1, 2)``. The number of rectangles must be less than :math:`2^{29}`. Parameters ---------- rectangles: :obj:`list` of :obj:`list` of :obj:`int` List of rectangles of the form ``[row_start, row_stop, col_start, col_stop]``. Returns ------- :class:`.BlockMatrix` Sparse block matrix. """ n_rectangles = len(rectangles) if n_rectangles >= (1 << 29): raise ValueError(f'number of rectangles must be less than 2^29, found {n_rectangles}') n_rows = self.n_rows n_cols = self.n_cols for r in rectangles: if len(r) != 4: raise ValueError(f'rectangle {r} does not have length 4') if not (0 <= r[0] <= r[1] <= n_rows and 0 <= r[2] <= r[3] <= n_cols): raise ValueError(f'rectangle {r} does not satisfy ' f'0 <= r[0] <= r[1] <= n_rows and 0 <= r[2] <= r[3] <= n_cols') flattened_rectangles = jarray(Env.jvm().long, list(itertools.chain(*rectangles)))
return BlockMatrix(self._jbm.filterRectangles(flattened_rectangles))
[docs] @staticmethod @typecheck(path_in=str, path_out=str, rectangles=sequenceof(sequenceof(int)), delimiter=str, n_partitions=nullable(int)) def export_rectangles(path_in, path_out, rectangles, delimiter='\t', n_partitions=None): """Export rectangular regions from a stored block matrix to delimited text files. Examples -------- Consider the following block matrix: >>> import numpy as np >>> nd = np.array([[ 1.0, 2.0, 3.0, 4.0], ... [ 5.0, 6.0, 7.0, 8.0], ... [ 9.0, 10.0, 11.0, 12.0], ... [13.0, 14.0, 15.0, 16.0]]) Filter to the three rectangles and write. >>> rectangles = [[0, 1, 0, 1], [0, 3, 0, 2], [1, 2, 0, 4]] >>> >>> (BlockMatrix.from_numpy(nd) ... .sparsify_rectangles(rectangles) ... .write('output/example.bm', force_row_major=True)) Export the three rectangles to TSV files: >>> BlockMatrix.export_rectangles( ... path_in='output/example.bm', ... path_out='output/example', ... rectangles = rectangles) This produces three files in the folder ``output/example``. The first file is ``rect-0_0-1-0-1``: .. code-block:: text 1.0 The second file is ``rect-1_0-3-0-2``: .. code-block:: text 1.0 2.0 5.0 6.0 9.0 10.0 The third file is ``rect-2_1-2-0-4``: .. code-block:: text 5.0 6.0 7.0 8.0 Warning ------- The block matrix must be stored in row-major format, as results from :meth:`.BlockMatrix.write` with ``force_row_major=True`` and from :meth:`.BlockMatrix.write_from_entry_expr`. Otherwise, :meth:`export` will fail. Notes ----- This method exports rectangular regions of a stored block matrix to delimited text files, in parallel by region. The block matrix can be sparse so long as all blocks overlapping the rectangles are present, i.e. this method does not currently support implicit zeros. Each rectangle is encoded as a list of length four of the form ``[row_start, row_stop, col_start, col_stop]``, where starts are inclusive and stops are exclusive. These must satisfy ``0 <= row_start <= row_stop <= n_rows`` and ``0 <= col_start <= col_stop <= n_cols``. For example ``[0, 2, 1, 3]`` corresponds to the row-index range ``[0, 2)`` and column-index range ``[1, 3)``, i.e. the elements at positions ``(0, 1)``, ``(0, 2)``, ``(1, 1)``, and ``(1, 2)``. Each file name encodes the index of the rectangle in `rectangles` and the bounds as formatted in the example. The number of rectangles must be less than :math:`2^{29}`. Parameters ---------- path_in: :obj:`srt` Path to input block matrix, stored row-major on disk. path_out: :obj:`str` Path for folder of exported files. rectangles: :obj:`list` of :obj:`list` of :obj:`int` List of rectangles of the form ``[row_start, row_stop, col_start, col_stop]``. delimiter: :obj:`str` Column delimiter. n_partitions: :obj:`int`, optional Maximum parallelism of export. Defaults to (and cannot exceed) the number of rectangles. """ n_rectangles = len(rectangles) if n_rectangles == 0: raise ValueError('no rectangles provided') if n_rectangles >= (1 << 29): raise ValueError(f'number of rectangles must be less than 2^29, found {n_rectangles}') if n_partitions is None: n_partitions = n_rectangles else: if n_partitions > n_rectangles: raise ValueError( f'n_partitions ({n_partitions}) cannot exceed the number of rectangles ({n_rectangles})') elif n_partitions < 0: raise ValueError(f'n_partitions must be positive, found {n_partitions}') meta = Env.hail().linalg.BlockMatrix.readMetadata(Env.hc()._jhc, path_in) n_rows = meta.nRows() n_cols = meta.nCols() for r in rectangles: if len(r) != 4: raise ValueError(f'rectangle {r} does not have length 4') if not (0 <= r[0] <= r[1] <= n_rows and 0 <= r[2] <= r[3] <= n_cols): raise ValueError(f'rectangle {r} does not satisfy ' f'0 <= r[0] <= r[1] <= n_rows and 0 <= r[2] <= r[3] <= n_cols') flattened_rectangles = jarray(Env.jvm().long, list(itertools.chain(*rectangles))) return Env.hail().linalg.BlockMatrix.exportRectangles(
Env.hc()._jhc, path_in, path_out, flattened_rectangles, delimiter, n_partitions) block_matrix_type.set(BlockMatrix) def _shape(b): if isinstance(b, int) or isinstance(b, float): return 1, 1 if isinstance(b, np.ndarray): b = _ndarray_as_2d(b) else: isinstance(b, BlockMatrix) return b.shape def _ndarray_as_2d(nd): if nd.ndim == 1: nd = nd.reshape(1, nd.shape[0]) elif nd.ndim > 2: raise ValueError(f'ndarray must have one or two axes, found shape {nd.shape}') return nd def _ndarray_as_float64(nd): if nd.dtype != np.float64: try: nd = nd.astype(np.float64) except ValueError as e: raise TypeError(f"ndarray elements of dtype {nd.dtype} cannot be converted to type 'float64'") from e return nd def _jarray_from_ndarray(nd): if nd.size >= (1 << 31): raise ValueError(f'size of ndarray must be less than 2^31, found {nd.size}') nd = _ndarray_as_float64(nd) path = new_local_temp_file() uri = local_path_uri(path) nd.tofile(path) return Env.hail().utils.richUtils.RichArray.importFromDoubles(Env.hc()._jhc, uri, nd.size) def _ndarray_from_jarray(ja): path = new_local_temp_file() uri = local_path_uri(path) Env.hail().utils.richUtils.RichArray.exportToDoubles(Env.hc()._jhc, uri, ja) return np.fromfile(path) def _breeze_fromfile(uri, n_rows, n_cols): n_entries = n_rows * n_cols if n_entries >= 1 << 31: raise ValueError(f'number of entries must be less than 2^31, found {n_entries}') return Env.hail().utils.richUtils.RichDenseMatrixDouble.importFromDoubles(Env.hc()._jhc, uri, n_rows, n_cols, True) def _breeze_from_ndarray(nd): if any(i == 0 for i in nd.shape): raise ValueError(f'from_numpy: ndarray dimensions must be non-zero, found shape {nd.shape}') nd = _ndarray_as_2d(nd) nd = _ndarray_as_float64(nd) n_rows, n_cols = nd.shape path = new_local_temp_file() uri = local_path_uri(path) nd.tofile(path) return _breeze_fromfile(uri, n_rows, n_cols)