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Documentation update (#12)
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‎cvmatrix/__init__.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1 +1 @@
1-
__version__ = "3.1.0"
1+
__version__ = "3.1.0.post1"

‎cvmatrix/cvmatrix.py‎

Lines changed: 64 additions & 60 deletions
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@@ -10,7 +10,7 @@
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E-mail: ocge@foss.dk
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"""
1212

13-
from typing import Tuple, Union
13+
from typing import Optional, Tuple, Union
1414

1515
import numpy as np
1616
from numpy import typing as npt
@@ -185,8 +185,8 @@ def __init__(
185185
def fit(
186186
self,
187187
X: npt.ArrayLike,
188-
Y: Union[None, npt.ArrayLike] = None,
189-
weights: Union[None, npt.ArrayLike] = None,
188+
Y: Optional[npt.ArrayLike] = None,
189+
weights: Optional[npt.ArrayLike] = None,
190190
) -> None:
191191
"""
192192
Loads and stores `X`, `Y`, and "weights", for cross-validation. Computes
@@ -208,6 +208,10 @@ def fit(
208208
Weights for each sample in `X` and `Y`. If `None`, no weights are used in
209209
the computations. If provided, the weights must be non-negative.
210210
211+
Returns
212+
-------
213+
None
214+
211215
Raises
212216
------
213217
ValueError
@@ -221,7 +225,7 @@ def fit(
221225
def training_XTX(
222226
self, validation_indices: npt.NDArray[np.int_]
223227
) -> Tuple[
224-
np.ndarray, Tuple[Union[None, np.ndarray], Union[None, np.ndarray], None, None]
228+
np.ndarray, Tuple[Optional[np.ndarray], Optional[np.ndarray], None, None]
225229
]:
226230
"""
227231
Computes the training set :math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{X}`
@@ -240,11 +244,11 @@ def training_XTX(
240244
241245
Returns
242246
-------
243-
Tuple of two elements. The first element is an array of shape (K, K)
244-
corresponding to the training set
245-
:math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{X}`. The second element is
246-
a tuple containing the row of column-wise weighted means for `X`, the row
247-
of column-wise weighted standard deviations for `X`, and two `None`
247+
Tuple of two elements.
248+
The first element is an array of shape (K, K) corresponding to the training
249+
set :math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{X}`. The second element
250+
is a tuple containing the row of column-wise weighted means for `X`, the
251+
row of column-wise weighted standard deviations for `X`, and two `None`
248252
corresponding to the non-computed rows of column-wise weighted means and
249253
standard deviations for `Y`. If a statistic is not computed, it is `None`.
250254
@@ -280,10 +284,10 @@ def training_XTX(
280284
def training_XTY(self, validation_indices: npt.NDArray[np.int_]) -> Tuple[
281285
np.ndarray,
282286
Tuple[
283-
Union[None, np.ndarray],
284-
Union[None, np.ndarray],
285-
Union[None, np.ndarray],
286-
Union[None, np.ndarray],
287+
Optional[np.ndarray],
288+
Optional[np.ndarray],
289+
Optional[np.ndarray],
290+
Optional[np.ndarray],
287291
],
288292
]:
289293
"""
@@ -305,9 +309,9 @@ def training_XTY(self, validation_indices: npt.NDArray[np.int_]) -> Tuple[
305309
306310
Returns
307311
-------
308-
Tuple of two elements. The first element is an array of shape (K, M)
309-
corresponding to the training set
310-
:math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{Y}`. The second element
312+
Tuple of two elements.
313+
The first element is an array of shape (K, M) corresponding to the training
314+
set :math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{Y}`. The second element
311315
is a tuple containing the row of column-wise weighted means for `X`, the
312316
row of column-wise weighted standard deviations for `X`, the row of
313317
column-wise weighted means for `Y`, and the row of column-wise weighted
@@ -344,10 +348,10 @@ def training_XTY(self, validation_indices: npt.NDArray[np.int_]) -> Tuple[
344348
def training_XTX_XTY(self, validation_indices: npt.NDArray[np.int_]) -> Tuple[
345349
Tuple[np.ndarray, np.ndarray],
346350
Tuple[
347-
Union[None, np.ndarray],
348-
Union[None, np.ndarray],
349-
Union[None, np.ndarray],
350-
Union[None, np.ndarray],
351+
Optional[np.ndarray],
352+
Optional[np.ndarray],
353+
Optional[np.ndarray],
354+
Optional[np.ndarray],
351355
],
352356
]:
353357
"""
@@ -372,9 +376,9 @@ def training_XTX_XTY(self, validation_indices: npt.NDArray[np.int_]) -> Tuple[
372376
373377
Returns
374378
-------
375-
Tuple of two tuples. The first tuple contains arrays of shapes (K, K) and
376-
(K, M). These are the training set
377-
:math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{X}` and
379+
Tuple of two tuples.
380+
The first tuple contains arrays of shapes (K, K) and (K, M). These are the
381+
training set :math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{X}` and
378382
:math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{Y}`. The second tuple
379383
contains the row of column-wise weighted means for `X`, the row of
380384
column-wise weighted standard deviations for `X`, the row of column-wise
@@ -408,10 +412,10 @@ def training_XTX_XTY(self, validation_indices: npt.NDArray[np.int_]) -> Tuple[
408412
return self._training_matrices(True, True, validation_indices)
409413

410414
def training_statistics(self, validation_indices: npt.NDArray[np.int_]) -> Tuple[
411-
Union[None, np.ndarray],
412-
Union[None, np.ndarray],
413-
Union[None, np.ndarray],
414-
Union[None, np.ndarray],
415+
Optional[np.ndarray],
416+
Optional[np.ndarray],
417+
Optional[np.ndarray],
418+
Optional[np.ndarray],
415419
]:
416420
"""
417421
Computes the row of column-wise weighted means and standard deviations for `X`
@@ -430,7 +434,7 @@ def training_statistics(self, validation_indices: npt.NDArray[np.int_]) -> Tuple
430434
431435
Returns
432436
-------
433-
Tuple of four elements of Union[None, np.ndarray]
437+
Tuple of four elements of Optional[np.ndarray]
434438
A tuple containing the row of column-wise weighted means for `X`, the row
435439
of column-wise weighted standard deviations for `X`, the row of column-wise
436440
weighted means for `Y`, and the row of column-wise weighted standard
@@ -509,20 +513,20 @@ def _get_sum_w_train_and_num_nonzero_w_train(
509513
def _compute_training_stats(
510514
self,
511515
val_indices: npt.NDArray[np.int_],
512-
X_val: Union[None, np.ndarray],
513-
X_val_unweighted: Union[None, np.ndarray],
514-
Y_val: Union[None, np.ndarray],
515-
Y_val_unweighted: Union[None, np.ndarray],
516+
X_val: Optional[np.ndarray],
517+
X_val_unweighted: Optional[np.ndarray],
518+
Y_val: Optional[np.ndarray],
519+
Y_val_unweighted: Optional[np.ndarray],
516520
return_X_mean: bool,
517521
return_X_std: bool,
518522
return_Y_mean: bool,
519523
return_Y_std: bool,
520524
) -> Tuple[
521-
Union[None, np.ndarray],
522-
Union[None, np.ndarray],
523-
Union[None, np.ndarray],
524-
Union[None, np.ndarray],
525-
Union[None, float],
525+
Optional[np.ndarray],
526+
Optional[np.ndarray],
527+
Optional[np.ndarray],
528+
Optional[np.ndarray],
529+
Optional[float],
526530
]:
527531
"""
528532
Computes the training set statistics. The training set corresponds
@@ -570,7 +574,7 @@ def _compute_training_stats(
570574
571575
Returns
572576
-------
573-
Tuple of Union[None, np.ndarray]
577+
Tuple of Optional[np.ndarray]
574578
A tuple containing the row of column-wise weighted means for `X`, the row
575579
of column-wise weighted standard deviations for `X`, the row of column-wise
576580
weighted means for `Y`, the row of column-wise weighted standard deviations
@@ -638,10 +642,10 @@ def _training_matrices(
638642
) -> Tuple[
639643
Union[np.ndarray, Tuple[np.ndarray, np.ndarray]],
640644
Tuple[
641-
Union[None, np.ndarray],
642-
Union[None, np.ndarray],
643-
Union[None, np.ndarray],
644-
Union[None, np.ndarray],
645+
Optional[np.ndarray],
646+
Optional[np.ndarray],
647+
Optional[np.ndarray],
648+
Optional[np.ndarray],
645649
],
646650
]:
647651
"""
@@ -669,15 +673,15 @@ def _training_matrices(
669673
670674
Returns
671675
-------
672-
Tuple of two elements. The first element is an array of shape (K, K) or (K, M)
673-
or a tuple of arrays of shapes (K, K) and (K, M). These are the training
674-
set :math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{X}` and/or
675-
training set :math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{Y}`. The
676-
second element is a tuple containing the row of column-wise weighted means
677-
for `X`, the row of column-wise weighted standard deviations for `X`, the
678-
row of column-wise weighted means for `Y`, and the row of column-wise
679-
weighted standard deviations for `Y`. If a statistic is not computed, it is
680-
`None`.
676+
Tuple of two elements.
677+
The first element is an array of shape (K, K) or (K, M) or a tuple of
678+
arrays of shapes (K, K) and (K, M). These are the training set
679+
:math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{X}` and/or training set
680+
:math:`\mathbf{X}^{\mathbf{T}}\mathbf{W}\mathbf{Y}`. The second element is
681+
a tuple containing the row of column-wise weighted means for `X`, the row
682+
of column-wise weighted standard deviations for `X`, the row of column-wise
683+
weighted means for `Y`, and the row of column-wise weighted standard
684+
deviations for `Y`. If a statistic is not computed, it is `None`.
681685
682686
Raises
683687
------
@@ -787,8 +791,8 @@ def _get_val_matrices(
787791
np.ndarray,
788792
np.ndarray,
789793
np.ndarray,
790-
Union[None, np.ndarray],
791-
Union[None, np.ndarray],
794+
Optional[np.ndarray],
795+
Optional[np.ndarray],
792796
]:
793797
"""
794798
Returns the validation set matrices for a given fold.
@@ -832,11 +836,11 @@ def _training_kernel_matrix(
832836
total_kernel_mat: np.ndarray,
833837
X_val: np.ndarray,
834838
mat2_val: np.ndarray,
835-
X_train_mean: Union[None, np.ndarray] = None,
836-
mat2_train_mean: Union[None, np.ndarray] = None,
837-
X_train_std: Union[None, np.ndarray] = None,
838-
mat2_train_std: Union[None, np.ndarray] = None,
839-
sum_w_train: Union[None, float] = None,
839+
X_train_mean: Optional[np.ndarray] = None,
840+
mat2_train_mean: Optional[np.ndarray] = None,
841+
X_train_std: Optional[np.ndarray] = None,
842+
mat2_train_std: Optional[np.ndarray] = None,
843+
sum_w_train: Optional[float] = None,
840844
center: bool = False,
841845
) -> np.ndarray:
842846
"""
@@ -1037,8 +1041,8 @@ def _init_mat(self, mat: np.ndarray) -> np.ndarray:
10371041
def _init_mats(
10381042
self,
10391043
X: npt.ArrayLike,
1040-
Y: Union[None, npt.ArrayLike],
1041-
weights: Union[None, npt.ArrayLike],
1044+
Y: Optional[npt.ArrayLike],
1045+
weights: Optional[npt.ArrayLike],
10421046
) -> None:
10431047
"""
10441048
Initializes the matrices `X`, `Y`, and `weights` with the provided

‎docs/conf.py‎

Lines changed: 6 additions & 2 deletions
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@@ -34,7 +34,11 @@
3434
templates_path = ["_templates"]
3535
exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
3636

37-
source_suffix = [".rst", ".md"]
37+
source_suffix = {
38+
".rst": "restructuredtext",
39+
".md": "markdown",
40+
}
41+
3842
master_doc = "index"
3943

4044
autosummary_generate = True
@@ -62,4 +66,4 @@
6266
"undoc-members": True,
6367
"show-inheritance": True,
6468
"private-members": False,
65-
}
69+
}

‎docs/source/cvmatrix.rst‎

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@@ -1,7 +1,8 @@
1-
cvmatrix module
2-
---------------
1+
CVMatrix Module
2+
===============
33
Module contents
44
---------------
55
.. toctree::
66

7-
cvmatrix_module
7+
cvmatrix_module
8+
partitioner_module

‎docs/source/partitioner_module.rst‎

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1+
.. automodule:: cvmatrix.partitioner
2+
3+
.. rubric:: Classes
4+
5+
.. autosummary::
6+
7+
Partitioner

‎pyproject.toml‎

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@@ -1,6 +1,6 @@
11
[tool.poetry]
22
name = "cvmatrix"
3-
version = "3.1.0"
3+
version = "3.1.0.post1"
44
description = "Fast computation of possibly weighted and possibly centered/scaled training set kernel matrices in a cross-validation setting."
55
authors = ["Sm00thix <oleemail@icloud.com>"]
66
maintainers = ["Sm00thix <oleemail@icloud.com>"]

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