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Lint pyttb_utils and lint/type sptensor #77

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merged 7 commits into from
Mar 20, 2023
Merged

Lint pyttb_utils and lint/type sptensor #77

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67cc48a
PYTTB_UTILS: Fix and enforce pylint
ntjohnson1 Feb 25, 2023
a1f24df
PYTTB_UTILS: Pull out utility only used internally in sptensor
ntjohnson1 Feb 25, 2023
6f384c4
SPTENSOR: Fix and enforce pylint
ntjohnson1 Feb 25, 2023
587d317
SPTENSOR: Initial pass a typing support
ntjohnson1 Feb 25, 2023
f5de9d8
SPTENSOR: Complete initial typing coverage
ntjohnson1 Mar 18, 2023
3410d15
SPTENSOR: Fix test coverage from typing changes.
ntjohnson1 Mar 18, 2023
7b75dd1
PYLINT: Update test to lint files in parallel to improve dev experience.
ntjohnson1 Mar 18, 2023
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194 changes: 69 additions & 125 deletions pyttb/pyttb_utils.py
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Original file line number Diff line number Diff line change
@@ -1,19 +1,19 @@
# Copyright 2022 National Technology & Engineering Solutions of Sandia,
# LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the
# U.S. Government retains certain rights in this software.

"""PYTTB shared utilities across tensor types"""
from inspect import signature
from typing import Optional, Tuple, overload

import numpy as np
import scipy.sparse as sparse

import pyttb as ttb


def tt_to_dense_matrix(tensorInstance, mode, transpose=False):
"""
Helper function to unwrap tensor into dense matrix, should replace the core need for tenmat
Helper function to unwrap tensor into dense matrix, should replace the core need
for tenmat

Parameters
----------
Expand Down Expand Up @@ -46,7 +46,8 @@ def tt_to_dense_matrix(tensorInstance, mode, transpose=False):

def tt_from_dense_matrix(matrix, shape, mode, idx):
"""
Helper function to wrap dense matrix into tensor. Inverse of :class:`pyttb.tt_to_dense_matrix`
Helper function to wrap dense matrix into tensor.
Inverse of :class:`pyttb.tt_to_dense_matrix`

Parameters
----------
Expand All @@ -70,63 +71,6 @@ def tt_from_dense_matrix(matrix, shape, mode, idx):
return tensorInstance


def tt_to_sparse_matrix(sptensorInstance, mode, transpose=False):
"""
Helper function to unwrap sptensor into sparse matrix, should replace the core need for sptenmat

Parameters
----------
sptensorInstance: :class:`pyttb.sptensor`
mode: int
Mode around which to unwrap tensor
transpose: bool
Whether or not to tranpose unwrapped tensor

Returns
-------
spmatrix: :class:`Scipy.sparse.coo_matrix`
"""
old = np.setdiff1d(np.arange(sptensorInstance.ndims), mode).astype(int)
spmatrix = sptensorInstance.reshape(
(np.prod(np.array(sptensorInstance.shape)[old]),), old
).spmatrix()
if transpose:
return spmatrix.transpose()
else:
return spmatrix


def tt_from_sparse_matrix(spmatrix, shape, mode, idx):
"""
Helper function to wrap sparse matrix into sptensor. Inverse of :class:`pyttb.tt_to_sparse_matrix`

Parameters
----------
spmatrix: :class:`Scipy.sparse.coo_matrix`
mode: int
Mode around which tensor was unwrapped
idx: int
in {0,1}, idx of mode in spmatrix, s.b. 0 for tranpose=True

Returns
-------
sptensorInstance: :class:`pyttb.sptensor`
"""
siz = np.array(shape)
old = np.setdiff1d(np.arange(len(shape)), mode).astype(int)
sptensorInstance = ttb.sptensor.from_tensor_type(sparse.coo_matrix(spmatrix))

# This expands the compressed dimension back to full size
sptensorInstance = sptensorInstance.reshape(siz[old], idx)
# This puts the modes in the right order, reshape places modified modes after the unchanged ones
sptensorInstance = sptensorInstance.reshape(
shape,
np.concatenate((np.arange(1, mode + 1), [0], np.arange(mode + 1, len(shape)))),
)

return sptensorInstance


def tt_union_rows(MatrixA, MatrixB):
"""
Helper function to reproduce functionality of MATLABS intersect(a,b,'rows')
Expand Down Expand Up @@ -206,8 +150,8 @@ def tt_dimscheck(
# Save dimensions of dims
P = len(dims)

# Reorder dims from smallest to largest
# (this matters in particular for the vector multiplicand case, where the order affects the result)
# Reorder dims from smallest to largest (this matters in particular for the vector
# multiplicand case, where the order affects the result)
sidx = np.argsort(dims)
sdims = dims[sidx]
vidx = None
Expand All @@ -217,24 +161,25 @@ def tt_dimscheck(
if M > N:
assert False, "Cannot have more multiplicands than dimensions"

# Check that the number of multiplicands must either be full dimensional or equal to the specified dimensions
# (M==N) or M(==P) respectively
if M != N and M != P:
# Check that the number of multiplicands must either be full dimensional or
# equal to the specified dimensions (M==N) or M(==P) respectively
if M not in (N, P):
assert False, "Invalid number of multiplicands"

# Check sizes to determine how to index multiplicands
if P == M:
# Case 1: Number of items in dims and number of multiplicands are equal; therfore, index in order of sdims
# Case 1: Number of items in dims and number of multiplicands are equal;
# therfore, index in order of sdims
vidx = sidx
else:
# Case 2: Number of multiplicands is equal to the number of dimensions of tensor;
# therefore, index multiplicands by dimensions in dims argument.
# Case 2: Number of multiplicands is equal to the number of dimensions of
# tensor; therefore, index multiplicands by dimensions in dims argument.
vidx = sdims

return sdims, vidx


def tt_tenfun(function_handle, *inputs):
def tt_tenfun(function_handle, *inputs): # pylint:disable=too-many-branches
"""
Apply a function to each element in a tensor

Expand All @@ -256,13 +201,13 @@ def tt_tenfun(function_handle, *inputs):
assert callable(function_handle), "function_handle must be callable"

# Convert inputs to tensors if they aren't already
for i in range(0, len(inputs)):
if isinstance(inputs[i], ttb.tensor) or isinstance(inputs[i], (float, int)):
for i, an_input in enumerate(inputs):
if isinstance(an_input, (ttb.tensor, float, int)):
continue
elif isinstance(inputs[i], np.ndarray):
inputs[i] = ttb.tensor.from_data(inputs[i])
if isinstance(an_input, np.ndarray):
inputs[i] = ttb.tensor.from_data(an_input)
elif isinstance(
inputs[i],
an_input,
(
ttb.ktensor,
ttb.ttensor,
Expand All @@ -272,11 +217,12 @@ def tt_tenfun(function_handle, *inputs):
ttb.symktensor,
),
):
inputs[i] = ttb.tensor.from_tensor_type(inputs[i])
inputs[i] = ttb.tensor.from_tensor_type(an_input)
else:
assert False, "Invalid input to ten fun"

# It's ok if there are two input and one is a scalar; otherwise all inputs have to be the same size
# It's ok if there are two input and one is a scalar; otherwise all inputs have to
# be the same size
if (
(len(inputs) == 2)
and isinstance(inputs[0], (float, int))
Expand All @@ -290,15 +236,15 @@ def tt_tenfun(function_handle, *inputs):
):
sz = inputs[0].shape
else:
for i in range(0, len(inputs)):
if isinstance(inputs[i], (float, int)):
assert False, "Argument {} is a scalar but expected a tensor".format(i)
for i, an_input in enumerate(inputs):
if isinstance(an_input, (float, int)):
assert False, f"Argument {i} is a scalar but expected a tensor"
elif i == 0:
sz = inputs[i].shape
elif sz != inputs[i].shape:
sz = an_input.shape
elif sz != an_input.shape:
assert (
False
), "Tensor {} is not the same size as the first tensor input".format(i)
), f"Tensor {i} is not the same size as the first tensor input"

# Number of inputs for function handle
nfunin = len(signature(function_handle).parameters)
Expand All @@ -322,8 +268,8 @@ def tt_tenfun(function_handle, *inputs):
X = np.reshape(X, (1, -1))
else:
X = np.zeros((len(inputs), np.prod(sz)))
for i in range(0, len(inputs)):
X[i, :] = np.reshape(inputs[i].data, (np.prod(sz)))
for i, an_input in enumerate(inputs):
X[i, :] = np.reshape(an_input.data, (np.prod(sz)))
data = function_handle(X)
data = np.reshape(data, sz)
Z = ttb.tensor.from_data(data)
Expand Down Expand Up @@ -395,7 +341,7 @@ def tt_intersect_rows(MatrixA, MatrixB):
return location[np.where(location >= 0)]


def tt_irenumber(t, shape, number_range):
def tt_irenumber(t, shape, number_range): # pylint: disable=unused-argument
"""
RENUMBER indices for sptensor subsasgn

Expand All @@ -409,25 +355,25 @@ def tt_irenumber(t, shape, number_range):
-------
newsubs: :class:`numpy.ndarray`
"""
# TODO shape is unused. Should it be used? I don't particularly understand what this is meant to be doing
# TODO shape is unused. Should it be used? I don't particularly understand what
# this is meant to be doing
nz = t.nnz
if nz == 0:
newsubs = np.array([])
return newsubs
else:
newsubs = t.subs.astype(int)
for i in range(0, len(number_range)):
r = number_range[i]
if isinstance(r, slice):
newsubs[:, i] = (newsubs[:, i])[r]
elif isinstance(r, int):
# This appears to be inserting new keys as rows to our subs here
newsubs = np.insert(newsubs, obj=i, values=r, axis=1)
else:
if isinstance(r, list):
r = np.array(r)
newsubs[:, i] = r[newsubs[:, i]]
return newsubs

newsubs = t.subs.astype(int)
for i, r in enumerate(number_range):
if isinstance(r, slice):
newsubs[:, i] = (newsubs[:, i])[r]
elif isinstance(r, int):
# This appears to be inserting new keys as rows to our subs here
newsubs = np.insert(newsubs, obj=i, values=r, axis=1)
else:
if isinstance(r, list):
r = np.array(r)
newsubs[:, i] = r[newsubs[:, i]]
return newsubs


def tt_assignment_type(x, subs, rhs):
Expand All @@ -444,13 +390,12 @@ def tt_assignment_type(x, subs, rhs):
-------
objectType
"""
if type(x) == type(rhs):
if type(x) is type(rhs):
return "subtensor"
# If subscripts is a tuple that contains an nparray
elif isinstance(subs, tuple) and len(subs) >= 2:
if isinstance(subs, tuple) and len(subs) >= 2:
return "subtensor"
else:
return "subscripts"
return "subscripts"


def tt_renumber(subs, shape, number_range):
Expand All @@ -476,8 +421,8 @@ def tt_renumber(subs, shape, number_range):
"""
newshape = np.array(shape)
newsubs = subs
for i in range(0, len(shape)):
if not (number_range[i] == slice(None, None, None)):
for i in range(0, len(shape)): # pylint: disable=consider-using-enumerate
if not number_range[i] == slice(None, None, None):
if subs.size == 0:
if not isinstance(number_range[i], slice):
if isinstance(number_range[i], (int, float)):
Expand Down Expand Up @@ -529,12 +474,14 @@ def tt_renumberdim(idx, shape, number_range):
return newidx, newshape


# TODO make more efficient, decide if we want to support the multiple response
# matlab does
# pylint: disable=line-too-long
# https://stackoverflow.com/questions/22699756/python-version-of-ismember-with-rows-and-index
# For thoughts on how to speed this up
def tt_ismember_rows(search, source):
"""
Find location of search rows in source array
https://stackoverflow.com/questions/22699756/python-version-of-ismember-with-rows-and-index
For thoughts on how to speed this up
#TODO make more efficient, decide if we want to support the multiple response matlab does

Parameters
----------
Expand All @@ -551,10 +498,10 @@ def tt_ismember_rows(search, source):
Examples
--------
>>> a = np.array([[4, 6], [1, 9], [2, 6]])
>>> b = np.array([[1, 7],[1, 8],[2, 6],[2, 1],[2, 4],[4, 6],[4, 7],[5, 9],[5, 2],[5, 1]])
>>> b = np.array([[2, 6],[2, 1],[2, 4],[4, 6],[4, 7],[5, 9],[5, 2],[5, 1]])
>>> results = tt_ismember_rows(a,b)
>>> print(results)
[ 5 -1 2]
[ 3 -1 0]

"""
results = np.ones(shape=search.shape[0]) * -1
Expand Down Expand Up @@ -585,7 +532,7 @@ def tt_ind2sub(shape: Tuple[int, ...], idx: np.ndarray) -> np.ndarray:
return np.array(np.unravel_index(idx, shape, order="F")).transpose()


def tt_subsubsref(obj, s):
def tt_subsubsref(obj, s): # pylint: disable=unused-argument
"""
Helper function for tensor toolbox subsref.

Expand All @@ -598,7 +545,8 @@ def tt_subsubsref(obj, s):
-------
Still uncertain to this functionality
"""
# TODO figure out when subsref yields key of length>1 for now ignore this logic and just return
# TODO figure out when subsref yields key of length>1 for now ignore this logic and
# just return
# if len(s) == 1:
# return obj
# else:
Expand All @@ -608,7 +556,8 @@ def tt_subsubsref(obj, s):

def tt_intvec2str(v):
"""
Print integer vector to a string with brackets. Numpy should already handle this so it is a placeholder stub
Print integer vector to a string with brackets. Numpy should already handle this so
it is a placeholder stub

Parameters
----------
Expand Down Expand Up @@ -774,10 +723,7 @@ def isrow(v):
-------
bool
"""
if v.ndim == 2 and v.shape[0] == 1 and v.shape[1] >= 1:
return True
else:
return False
return v.ndim == 2 and v.shape[0] == 1 and v.shape[1] >= 1


def isvector(a):
Expand All @@ -794,13 +740,11 @@ def isvector(a):
-------
bool
"""
if a.ndim == 1 or (a.ndim == 2 and (a.shape[0] == 1 or a.shape[1] == 1)):
return True
else:
return False
return a.ndim == 1 or (a.ndim == 2 and (a.shape[0] == 1 or a.shape[1] == 1))


# TODO: this is a challenge, since it may need to apply to either Python built in types or numpy types
# TODO: this is a challenge, since it may need to apply to either Python built in types
# or numpy types
def islogical(a):
"""
ISLOGICAL Checks if vector is a logical vector.
Expand All @@ -815,4 +759,4 @@ def islogical(a):
-------
bool
"""
return type(a) == bool
return isinstance(a, bool)
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