Compare ⋅ probabilistic-numerics/probnum

Overview Diff Coverage Changes Files Commits

No flags found

Use flags to group coverage reports by test type, project and/or folders.
Then setup custom commit statuses and notifications for each flag.

e.g., #unittest #integration

#production #enterprise

#frontend #backend

Learn more about Codecov Flags here.

Showing 1 of 2 files from the diff.

94.12% 100.00% +0.18%

.../probnum/problems/zoo/linalg/_random_spd_matrix.py changed.

Other files ignored by Codecov

...ems/test_zoo/test_linalg/test_random_spd_matrix.py has changed.

@@ -97,16 +97,16 @@

Loading

    density: float,
    chol_entry_min: float = 0.1,
    chol_entry_max: float = 1.0,
    format="csr",
    format="csr",  # pylint: disable="redefined-builtin"
    random_state: Optional[RandomStateArgType] = None,
) -> np.ndarray:
    """Random sparse symmetric positive definite matrix.
    r"""Random sparse symmetric positive definite matrix.

    Constructs a random sparse symmetric positive definite matrix for a given degree
    of sparsity. The matrix is constructed from its Cholesky factor :math:`L`. Its
    diagonal is set to one and all other entries of the lower triangle are sampled
    from a uniform distribution with bounds :code:`[chol_entry_min, chol_entry_max]`.
    The resulting sparse matrix is then given by :math:`A=LL^\\top`.
    diagonal is set to one and all other nonzero entries of the lower triangle are
    sampled from a uniform distribution with bounds :code:`[chol_entry_min,
    chol_entry_max]`. The resulting sparse matrix is then given by :math:`A=LL^\top`.

    Parameters
    ----------

@@ -136,34 +136,35 @@

Loading

    >>> sparsemat = random_sparse_spd_matrix(dim=5, density=0.1, random_state=42)
    >>> sparsemat.todense()
    matrix([[1.        , 0.        , 0.        , 0.        , 0.        ],
            [0.        , 1.        , 0.        , 0.30424224, 0.        ],
            [0.        , 1.        , 0.        , 0.37381802, 0.        ],
            [0.        , 0.        , 1.        , 0.        , 0.        ],
            [0.        , 0.30424224, 0.        , 1.09256334, 0.        ],
            [0.        , 0.37381802, 0.        , 1.13973991, 0.        ],
            [0.        , 0.        , 0.        , 0.        , 1.        ]])
    """

    # Initialization
    random_state = _utils.as_random_state(random_state)
    if not 0 <= density <= 1:
        raise ValueError(f"Density must be between 0 and 1, but is {density}.")
    chol = scipy.sparse.eye(dim, format=format)
    chol = scipy.sparse.eye(dim, format="csr")
    num_off_diag_cholesky = int(0.5 * dim * (dim - 1))
    num_nonzero_entries = int(num_off_diag_cholesky * density)

    if num_nonzero_entries > 0:
        # Samples sparse (non-symmetric) (n, n) matrix

        sparse_matrix = scipy.sparse.rand(
            m=dim,
            n=dim,
            format=format,
            density=0.5 * density,
            format="csr",
            density=density,
            random_state=random_state,
        )

        # Symmetrize sparse matrix
        sparse_matrix += sparse_matrix.T
        # Rescale entries
        sparse_matrix.data *= chol_entry_max - chol_entry_min
        sparse_matrix.data += chol_entry_min

        # Extract lower triangle
        chol += scipy.sparse.tril(A=sparse_matrix, k=-1, format=format)

    return chol @ chol.T
    return (chol @ chol.T).asformat(format=format)

Everything is accounted for!

No changes detected that need to be reviewed.

What changes does Codecov check for?

Lines, not adjusted in diff, that have changed coverage data.

Files that introduced coverage data that had none before.

Files that have missing coverage data that once were tracked.

84.40% 100.00% +<.01%

Improved implementation of `random_sparse_spd_matrix` (#457)

JonathanWenger

a060bd6

CI Passed

+1

84.39% 100.00% +0.19%

Perturbed-Step Solver and testing (#382)

NinaEffenberger

424520d

CI Passed

Files		•	•	•	Coverage
src/probnum	+1	+1	ø	ø	^+<.01% 84.40%
Project Totals (107 files)	5,313	4,484	218	611	84.40%

136	136		>>> sparsemat = random_sparse_spd_matrix(dim=5, density=0.1, random_state=42)
137	137		>>> sparsemat.todense()
138	138		matrix([[1. , 0. , 0. , 0. , 0. ],
139		-	[0. , 1. , 0. , 0.30424224, 0. ],
	139	+	[0. , 1. , 0. , 0.37381802, 0. ],
140	140		[0. , 0. , 1. , 0. , 0. ],
141		-	[0. , 0.30424224, 0. , 1.09256334, 0. ],
	141	+	[0. , 0.37381802, 0. , 1.13973991, 0. ],
142	142		[0. , 0. , 0. , 0. , 1. ]])
143	143		"""
144	144
145	145		# Initialization
146	146		random_state = _utils.as_random_state(random_state)
147	147		if not 0 <= density <= 1:
148	148		raise ValueError(f"Density must be between 0 and 1, but is {density}.")
149		-	chol = scipy.sparse.eye(dim, format=format)
	149	+	chol = scipy.sparse.eye(dim, format="csr")
150	150		num_off_diag_cholesky = int(0.5 * dim * (dim - 1))
151	151		num_nonzero_entries = int(num_off_diag_cholesky * density)
152	152
153	153		if num_nonzero_entries > 0:
154		-	# Samples sparse (non-symmetric) (n, n) matrix
	154	+
155	155		sparse_matrix = scipy.sparse.rand(
156	156		m=dim,
157	157		n=dim,
158		-	format=format,
159		-	density=0.5 * density,
	158	+	format="csr",
	159	+	density=density,
160	160		random_state=random_state,
161	161		)
162	162
163		-	# Symmetrize sparse matrix
164		-	sparse_matrix += sparse_matrix.T
	163	+	# Rescale entries
	164	+	sparse_matrix.data *= chol_entry_max - chol_entry_min
	165	+	sparse_matrix.data += chol_entry_min
165	166
166	167		# Extract lower triangle
167	168		chol += scipy.sparse.tril(A=sparse_matrix, k=-1, format=format)
168	169
169		-	return chol @ chol.T
	170	+	return (chol @ chol.T).asformat(format=format)

Navigation	Overlay
`t` Navigate files	`h` Toggle hits
`y` Change url to tip of branch	`m` Toggle misses
`b / v` Jump to prev/next hit line	`p` Toggle partial
`z / x` Jump to prev/next missed or partial line	`1..9` Toggle flags
`shift + o` Open current page in GitHub	`a` Toggle all on
`/ or ?` Show keyboard shortcuts dialog	`c` Toggle context lines or commits

Compare `424520d ... +0 ... a060bd6`

No flags found

Everything is accounted for!

What changes does Codecov check for?

2 Commits

97	97		density: float,
98	98		chol_entry_min: float = 0.1,
99	99		chol_entry_max: float = 1.0,
100		-	format="csr",
	100	+	format="csr", # pylint: disable="redefined-builtin"
101	101		random_state: Optional[RandomStateArgType] = None,
102	102		) -> np.ndarray:
103		-	"""Random sparse symmetric positive definite matrix.
	103	+	r"""Random sparse symmetric positive definite matrix.
104	104
105	105		Constructs a random sparse symmetric positive definite matrix for a given degree
106	106		of sparsity. The matrix is constructed from its Cholesky factor :math:`L`. Its
107		-	diagonal is set to one and all other entries of the lower triangle are sampled
108		-	from a uniform distribution with bounds :code:`[chol_entry_min, chol_entry_max]`.
109		-	The resulting sparse matrix is then given by :math:`A=LL^\\top`.
	107	+	diagonal is set to one and all other nonzero entries of the lower triangle are
	108	+	sampled from a uniform distribution with bounds :code:`[chol_entry_min,
	109	+	chol_entry_max]`. The resulting sparse matrix is then given by :math:`A=LL^\top`.
110	110
111	111		Parameters
112	112		----------

Compare 424520d ... +0 ... a060bd6

No flags found

Everything is accounted for!

What changes does Codecov check for?

2 Commits

Compare `424520d ... +0 ... a060bd6`