Source code for pymc.math

#   Copyright 2020 The PyMC Developers
#   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,
#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#   See the License for the specific language governing permissions and
#   limitations under the License.

import sys
import warnings

from functools import partial, reduce

import numpy as np
import pytensor
import pytensor.sparse
import pytensor.tensor as at
import pytensor.tensor.slinalg  # pylint: disable=unused-import
import scipy as sp
import scipy.sparse  # pylint: disable=unused-import

from pytensor.graph.basic import Apply
from pytensor.graph.op import Op

# pylint: disable=unused-import
from pytensor.tensor import (

    from pytensor.tensor.basic import extract_diag
except ImportError:
    from pytensor.tensor.nlinalg import extract_diag

from pytensor.tensor.nlinalg import det, matrix_dot, matrix_inverse, trace
from scipy.linalg import block_diag as scipy_block_diag

from pymc.pytensorf import floatX, ix_, largest_common_dtype

# pylint: enable=unused-import

__all__ = [

def kronecker(*Ks):
    r"""Return the Kronecker product of arguments:
          :math:`K_1 \otimes K_2 \otimes ... \otimes K_D`

    Ks : Iterable of 2D array-like
        Arrays of which to take the product.

    np.ndarray :
        Block matrix Kroncker product of the argument matrices.
    return reduce(at.slinalg.kron, Ks)

def cartesian(*arrays):
    """Makes the Cartesian product of arrays.

    arrays: N-D array-like
            N-D arrays where earlier arrays loop more slowly than later ones
    N = len(arrays)
    arrays_np = [np.asarray(x) for x in arrays]
    arrays_2d = [x[:, None] if np.asarray(x).ndim == 1 else x for x in arrays_np]
    arrays_integer = [np.arange(len(x)) for x in arrays_2d]
    product_integers = np.stack(np.meshgrid(*arrays_integer, indexing="ij"), -1).reshape(-1, N)
    return np.concatenate(
        [array[product_integers[:, i]] for i, array in enumerate(arrays_2d)], axis=-1

def kron_matrix_op(krons, m, op):
    r"""Apply op to krons and m in a way that reproduces ``op(kronecker(*krons), m)``

    krons : list of square 2D array-like objects
        D square matrices :math:`[A_1, A_2, ..., A_D]` to be Kronecker'ed
        :math:`A = A_1 \otimes A_2 \otimes ... \otimes A_D`
        Product of column dimensions must be :math:`N`
    m : NxM array or 1D array (treated as Nx1)
        Object that krons act upon

    numpy array

    def flat_matrix_op(flat_mat, mat):
        Nmat = mat.shape[1]
        flat_shape = flat_mat.shape
        mat2 = flat_mat.reshape((Nmat, -1))
        return op(mat, mat2).T.reshape(flat_shape)

    def kron_vector_op(v):
        return reduce(flat_matrix_op, krons, v)

    if m.ndim == 1:
        m = m[:, None]  # Treat 1D array as Nx1 matrix
    if m.ndim != 2:  # Has not been tested otherwise
        raise ValueError(f"m must have ndim <= 2, not {m.ndim}")
    result = kron_vector_op(m)
    result_shape = result.shape
    return at.reshape(result, (result_shape[1], result_shape[0])).T

# Define kronecker functions that work on 1D and 2D arrays
kron_dot = partial(kron_matrix_op,
kron_solve_lower = partial(kron_matrix_op, op=at.slinalg.SolveTriangular(lower=True))
kron_solve_upper = partial(kron_matrix_op, op=at.slinalg.SolveTriangular(lower=False))

def flat_outer(a, b):
    return at.outer(a, b).ravel()

def kron_diag(*diags):
    """Returns diagonal of a kronecker product.

    diags: 1D arrays
           The diagonals of matrices that are to be Kroneckered
    return reduce(flat_outer, diags)

def tround(*args, **kwargs):
    Temporary function to silence round warning in PyTensor. Please remove
    when the warning disappears.
    kwargs["mode"] = "half_to_even"
    return at.round(*args, **kwargs)

def logdiffexp(a, b):
    """log(exp(a) - exp(b))"""
    return a + at.log1mexp(b - a)

def logdiffexp_numpy(a, b):
    """log(exp(a) - exp(b))"""
    return a + log1mexp_numpy(b - a, negative_input=True)

[docs]def invlogit(x, eps=None): """The inverse of the logit function, 1 / (1 + exp(-x)).""" if eps is not None: warnings.warn( "pymc.math.invlogit no longer supports the ``eps`` argument and it will be ignored.", FutureWarning, stacklevel=2, ) return at.sigmoid(x)
def softmax(x, axis=None): # Ignore vector case UserWarning issued by PyTensor. This can be removed once PyTensor # drops that warning with warnings.catch_warnings(): warnings.simplefilter("ignore", UserWarning) return at.special.softmax(x, axis=axis) def log_softmax(x, axis=None): # Ignore vector case UserWarning issued by PyTensor. This can be removed once PyTensor # drops that warning with warnings.catch_warnings(): warnings.simplefilter("ignore", UserWarning) return at.special.log_softmax(x, axis=axis) def logbern(log_p): if np.isnan(log_p): raise FloatingPointError("log_p can't be nan.") return np.log(np.random.uniform()) < log_p
[docs]def logit(p): return at.log(p / (floatX(1) - p))
def log1mexp(x, *, negative_input=False): r"""Return log(1 - exp(-x)). This function is numerically more stable than the naive approach. For details, see References ---------- .. [Machler2012] Martin M├Ąchler (2012). "Accurately computing `\log(1-\exp(- \mid a \mid))` Assessed by the Rmpfr package" """ if not negative_input: warnings.warn( "pymc.math.log1mexp will expect a negative input in a future " "version of PyMC.\n To suppress this warning set `negative_input=True`", FutureWarning, stacklevel=2, ) x = -x return at.log1mexp(x) def log1mexp_numpy(x, *, negative_input=False): """Return log(1 - exp(x)). This function is numerically more stable than the naive approach. For details, see """ x = np.asarray(x, dtype="float") if not negative_input: warnings.warn( "pymc.math.log1mexp_numpy will expect a negative input in a future " "version of PyMC.\n To suppress this warning set `negative_input=True`", FutureWarning, stacklevel=2, ) x = -x out = np.empty_like(x) mask = x < -0.6931471805599453 # log(1/2) out[mask] = np.log1p(-np.exp(x[mask])) mask = ~mask out[mask] = np.log(-np.expm1(x[mask])) return out def flatten_list(tensors): return at.concatenate([var.ravel() for var in tensors]) class LogDet(Op): r"""Compute the logarithm of the absolute determinant of a square matrix M, log(abs(det(M))) on the CPU. Avoids det(M) overflow/ underflow. Notes ----- Once PR #3959 ( by harpone is merged, this must be removed. """ def make_node(self, x): x = pytensor.tensor.as_tensor_variable(x) o = pytensor.tensor.scalar(dtype=x.dtype) return Apply(self, [x], [o]) def perform(self, node, inputs, outputs, params=None): try: (x,) = inputs (z,) = outputs s = np.linalg.svd(x, compute_uv=False) log_det = np.sum(np.log(np.abs(s))) z[0] = np.asarray(log_det, dtype=x.dtype) except Exception: print(f"Failed to compute logdet of {x}.", file=sys.stdout) raise def grad(self, inputs, g_outputs): [gz] = g_outputs [x] = inputs return [gz * matrix_inverse(x).T] def __str__(self): return "LogDet" logdet = LogDet()
[docs]def probit(p): return -sqrt(2.0) * erfcinv(2.0 * p)
[docs]def invprobit(x): return 0.5 * erfc(-x / sqrt(2.0))
[docs]def expand_packed_triangular(n, packed, lower=True, diagonal_only=False): r"""Convert a packed triangular matrix into a two dimensional array. Triangular matrices can be stored with better space efficiency by storing the non-zero values in a one-dimensional array. We number the elements by row like this (for lower or upper triangular matrices): [[0 - - -] [[0 1 2 3] [1 2 - -] [- 4 5 6] [3 4 5 -] [- - 7 8] [6 7 8 9]] [- - - 9] Parameters ---------- n: int The number of rows of the triangular matrix. packed: pytensor.vector The matrix in packed format. lower: bool, default=True If true, assume that the matrix is lower triangular. diagonal_only: bool If true, return only the diagonal of the matrix. """ if packed.ndim != 1: raise ValueError("Packed triangular is not one dimensional.") if not isinstance(n, int): raise TypeError("n must be an integer") if diagonal_only and lower: diag_idxs = np.arange(1, n + 1).cumsum() - 1 return packed[diag_idxs] elif diagonal_only and not lower: diag_idxs = np.arange(2, n + 2)[::-1].cumsum() - n - 1 return packed[diag_idxs] elif lower: out = at.zeros((n, n), dtype=pytensor.config.floatX) idxs = np.tril_indices(n) return at.set_subtensor(out[idxs], packed) elif not lower: out = at.zeros((n, n), dtype=pytensor.config.floatX) idxs = np.triu_indices(n) return at.set_subtensor(out[idxs], packed)
class BatchedDiag(Op): """ Fast BatchedDiag allocation """ __props__ = () def make_node(self, diag): diag = at.as_tensor_variable(diag) if diag.type.ndim != 2: raise TypeError("data argument must be a matrix", diag.type) return Apply(self, [diag], [at.tensor3(dtype=diag.dtype)]) def perform(self, node, ins, outs, params=None): (C,) = ins (z,) = outs bc = C.shape[0] dim = C.shape[-1] Cd = np.zeros((bc, dim, dim), C.dtype) bidx = np.repeat(np.arange(bc), dim) didx = np.tile(np.arange(dim), bc) Cd[bidx, didx, didx] = C.flatten() z[0] = Cd def grad(self, inputs, gout): (gz,) = gout idx = at.arange(gz.shape[-1]) return [gz[..., idx, idx]] def infer_shape(self, fgraph, nodes, shapes): return [(shapes[0][0],) + (shapes[0][1],) * 2] def batched_diag(C): C = at.as_tensor(C) dim = C.shape[-1] if C.ndim == 2: # diag -> matrices return BatchedDiag()(C) elif C.ndim == 3: # matrices -> diag idx = at.arange(dim) return C[..., idx, idx] else: raise ValueError("Input should be 2 or 3 dimensional") class BlockDiagonalMatrix(Op): __props__ = ("sparse", "format") def __init__(self, sparse=False, format="csr"): if format not in ("csr", "csc"): raise ValueError(f"format must be one of: 'csr', 'csc', got {format}") self.sparse = sparse self.format = format def make_node(self, *matrices): if not matrices: raise ValueError("no matrices to allocate") matrices = list(map(at.as_tensor, matrices)) if any(mat.type.ndim != 2 for mat in matrices): raise TypeError("all data arguments must be matrices") if self.sparse: out_type = pytensor.sparse.matrix(self.format, dtype=largest_common_dtype(matrices)) else: out_type = pytensor.tensor.matrix(dtype=largest_common_dtype(matrices)) return Apply(self, matrices, [out_type]) def perform(self, node, inputs, output_storage, params=None): dtype = largest_common_dtype(inputs) if self.sparse: output_storage[0][0] = sp.sparse.block_diag(inputs, self.format, dtype) else: output_storage[0][0] = scipy_block_diag(*inputs).astype(dtype) def grad(self, inputs, gout): shapes = at.stack([i.shape for i in inputs]) index_end = shapes.cumsum(0) index_begin = index_end - shapes slices = [ ix_( at.arange(index_begin[i, 0], index_end[i, 0]), at.arange(index_begin[i, 1], index_end[i, 1]), ) for i in range(len(inputs)) ] return [gout[0][slc] for slc in slices] def infer_shape(self, fgraph, nodes, shapes): first, second = zip(*shapes) return [(at.add(*first), at.add(*second))] def block_diagonal(matrices, sparse=False, format="csr"): r"""See scipy.sparse.block_diag or scipy.linalg.block_diag for reference Parameters ---------- matrices: tensors format: str (default 'csr') must be one of: 'csr', 'csc' sparse: bool (default False) if True return sparse format Returns ------- matrix """ if len(matrices) == 1: # graph optimization return matrices[0] return BlockDiagonalMatrix(sparse=sparse, format=format)(*matrices)