-
Notifications
You must be signed in to change notification settings - Fork 388
/
precision_recall.py
462 lines (374 loc) · 21.2 KB
/
precision_recall.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
# Copyright The PyTorch Lightning team.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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.
from typing import Optional
import torch
from torchmetrics.classification.stat_scores import _reduce_stat_scores
from torchmetrics.functional.classification.stat_scores import _stat_scores_update
def _precision_compute(
tp: torch.Tensor,
fp: torch.Tensor,
tn: torch.Tensor,
fn: torch.Tensor,
average: str,
mdmc_average: Optional[str],
) -> torch.Tensor:
return _reduce_stat_scores(
numerator=tp,
denominator=tp + fp,
weights=None if average != "weighted" else tp + fn,
average=average,
mdmc_average=mdmc_average,
)
def precision(
preds: torch.Tensor,
target: torch.Tensor,
average: str = "micro",
mdmc_average: Optional[str] = None,
ignore_index: Optional[int] = None,
num_classes: Optional[int] = None,
threshold: float = 0.5,
top_k: Optional[int] = None,
is_multiclass: Optional[bool] = None,
) -> torch.Tensor:
r"""
Computes `Precision <https://en.wikipedia.org/wiki/Precision_and_recall>`_:
.. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}}
Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and
false positives respecitively. With the use of ``top_k`` parameter, this metric can
generalize to Precision@K.
The reduction method (how the precision scores are aggregated) is controlled by the
``average`` parameter, and additionally by the ``mdmc_average`` parameter in the
multi-dimensional multi-class case. Accepts all inputs listed in :ref:`references/modules:input types`.
Args:
preds: Predictions from model (probabilities or labels)
target: Ground truth values
average:
Defines the reduction that is applied. Should be one of the following:
- ``'micro'`` [default]: Calculate the metric globally, accross all samples and classes.
- ``'macro'``: Calculate the metric for each class separately, and average the
metrics accross classes (with equal weights for each class).
- ``'weighted'``: Calculate the metric for each class separately, and average the
metrics accross classes, weighting each class by its support (``tp + fn``).
- ``'none'`` or ``None``: Calculate the metric for each class separately, and return
the metric for every class.
- ``'samples'``: Calculate the metric for each sample, and average the metrics
across samples (with equal weights for each sample).
Note that what is considered a sample in the multi-dimensional multi-class case
depends on the value of ``mdmc_average``.
class_reduction:
.. warning :: This parameter is deprecated, use ``average``. Will be removed in v1.4.0.
mdmc_average:
Defines how averaging is done for multi-dimensional multi-class inputs (on top of the
``average`` parameter). Should be one of the following:
- ``None`` [default]: Should be left unchanged if your data is not multi-dimensional
multi-class.
- ``'samplewise'``: In this case, the statistics are computed separately for each
sample on the ``N`` axis, and then averaged over samples.
The computation for each sample is done by treating the flattened extra axes ``...``
(see :ref:`references/modules:input types`) as the ``N`` dimension within the sample,
and computing the metric for the sample based on that.
- ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
(see :ref:`references/modules:input types`)
are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
ignore_index:
Integer specifying a target class to ignore. If given, this class index does not contribute
to the returned score, regardless of reduction method. If an index is ignored, and ``average=None``
or ``'none'``, the score for the ignored class will be returned as ``nan``.
num_classes:
Number of classes. Necessary for ``'macro'``, ``'weighted'`` and ``None`` average methods.
threshold:
Threshold probability value for transforming probability predictions to binary
(0,1) predictions, in the case of binary or multi-label inputs.
top_k:
Number of highest probability entries for each sample to convert to 1s - relevant
only for inputs with probability predictions. If this parameter is set for multi-label
inputs, it will take precedence over ``threshold``. For (multi-dim) multi-class inputs,
this parameter defaults to 1.
Should be left unset (``None``) for inputs with label predictions.
is_multiclass:
Used only in certain special cases, where you want to treat inputs as a different type
than what they appear to be. See the parameter's
:ref:`documentation section <references/modules:using the is_multiclass parameter>`
for a more detailed explanation and examples.
Return:
The shape of the returned tensor depends on the ``average`` parameter
- If ``average in ['micro', 'macro', 'weighted', 'samples']``, a one-element tensor will be returned
- If ``average in ['none', None]``, the shape will be ``(C,)``, where ``C`` stands for the number
of classes
Example:
>>> from torchmetrics.functional import precision
>>> preds = torch.tensor([2, 0, 2, 1])
>>> target = torch.tensor([1, 1, 2, 0])
>>> precision(preds, target, average='macro', num_classes=3)
tensor(0.1667)
>>> precision(preds, target, average='micro')
tensor(0.2500)
"""
allowed_average = ["micro", "macro", "weighted", "samples", "none", None]
if average not in allowed_average:
raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
allowed_mdmc_average = [None, "samplewise", "global"]
if mdmc_average not in allowed_mdmc_average:
raise ValueError(f"The `mdmc_average` has to be one of {allowed_mdmc_average}, got {mdmc_average}.")
if average in ["macro", "weighted", "none", None] and (not num_classes or num_classes < 1):
raise ValueError(f"When you set `average` as {average}, you have to provide the number of classes.")
if num_classes and ignore_index is not None and (not 0 <= ignore_index < num_classes or num_classes == 1):
raise ValueError(f"The `ignore_index` {ignore_index} is not valid for inputs with {num_classes} classes")
reduce = "macro" if average in ["weighted", "none", None] else average
tp, fp, tn, fn = _stat_scores_update(
preds,
target,
reduce=reduce,
mdmc_reduce=mdmc_average,
threshold=threshold,
num_classes=num_classes,
top_k=top_k,
is_multiclass=is_multiclass,
ignore_index=ignore_index,
)
return _precision_compute(tp, fp, tn, fn, average, mdmc_average)
def _recall_compute(
tp: torch.Tensor,
fp: torch.Tensor,
tn: torch.Tensor,
fn: torch.Tensor,
average: str,
mdmc_average: Optional[str],
) -> torch.Tensor:
return _reduce_stat_scores(
numerator=tp,
denominator=tp + fn,
weights=None if average != "weighted" else tp + fn,
average=average,
mdmc_average=mdmc_average,
)
def recall(
preds: torch.Tensor,
target: torch.Tensor,
average: str = "micro",
mdmc_average: Optional[str] = None,
ignore_index: Optional[int] = None,
num_classes: Optional[int] = None,
threshold: float = 0.5,
top_k: Optional[int] = None,
is_multiclass: Optional[bool] = None,
) -> torch.Tensor:
r"""
Computes `Recall <https://en.wikipedia.org/wiki/Precision_and_recall>`_:
.. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}}
Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and
false negatives respecitively. With the use of ``top_k`` parameter, this metric can
generalize to Recall@K.
The reduction method (how the recall scores are aggregated) is controlled by the
``average`` parameter, and additionally by the ``mdmc_average`` parameter in the
multi-dimensional multi-class case. Accepts all inputs listed in :ref:`references/modules:input types`.
Args:
preds: Predictions from model (probabilities, or labels)
target: Ground truth values
average:
Defines the reduction that is applied. Should be one of the following:
- ``'micro'`` [default]: Calculate the metric globally, accross all samples and classes.
- ``'macro'``: Calculate the metric for each class separately, and average the
metrics accross classes (with equal weights for each class).
- ``'weighted'``: Calculate the metric for each class separately, and average the
metrics accross classes, weighting each class by its support (``tp + fn``).
- ``'none'`` or ``None``: Calculate the metric for each class separately, and return
the metric for every class.
- ``'samples'``: Calculate the metric for each sample, and average the metrics
across samples (with equal weights for each sample).
Note that what is considered a sample in the multi-dimensional multi-class case
depends on the value of ``mdmc_average``.
mdmc_average:
Defines how averaging is done for multi-dimensional multi-class inputs (on top of the
``average`` parameter). Should be one of the following:
- ``None`` [default]: Should be left unchanged if your data is not multi-dimensional
multi-class.
- ``'samplewise'``: In this case, the statistics are computed separately for each
sample on the ``N`` axis, and then averaged over samples.
The computation for each sample is done by treating the flattened extra axes ``...``
(see :ref:`references/modules:input types`) as the ``N`` dimension within the sample,
and computing the metric for the sample based on that.
- ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
(see :ref:`references/modules:input types`)
are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
ignore_index:
Integer specifying a target class to ignore. If given, this class index does not contribute
to the returned score, regardless of reduction method. If an index is ignored, and ``average=None``
or ``'none'``, the score for the ignored class will be returned as ``nan``.
num_classes:
Number of classes. Necessary for ``'macro'``, ``'weighted'`` and ``None`` average methods.
threshold:
Threshold probability value for transforming probability predictions to binary
(0,1) predictions, in the case of binary or multi-label inputs
top_k:
Number of highest probability entries for each sample to convert to 1s - relevant
only for inputs with probability predictions. If this parameter is set for multi-label
inputs, it will take precedence over ``threshold``. For (multi-dim) multi-class inputs,
this parameter defaults to 1.
Should be left unset (``None``) for inputs with label predictions.
is_multiclass:
Used only in certain special cases, where you want to treat inputs as a different type
than what they appear to be. See the parameter's
:ref:`documentation section <references/modules:using the is_multiclass parameter>`
for a more detailed explanation and examples.
Return:
The shape of the returned tensor depends on the ``average`` parameter
- If ``average in ['micro', 'macro', 'weighted', 'samples']``, a one-element tensor will be returned
- If ``average in ['none', None]``, the shape will be ``(C,)``, where ``C`` stands for the number
of classes
Example:
>>> from torchmetrics.functional import recall
>>> preds = torch.tensor([2, 0, 2, 1])
>>> target = torch.tensor([1, 1, 2, 0])
>>> recall(preds, target, average='macro', num_classes=3)
tensor(0.3333)
>>> recall(preds, target, average='micro')
tensor(0.2500)
"""
allowed_average = ["micro", "macro", "weighted", "samples", "none", None]
if average not in allowed_average:
raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
allowed_mdmc_average = [None, "samplewise", "global"]
if mdmc_average not in allowed_mdmc_average:
raise ValueError("The `mdmc_average` has to be one of {allowed_mdmc_average}, got {mdmc_average}.")
if average in ["macro", "weighted", "none", None] and (not num_classes or num_classes < 1):
raise ValueError(f"When you set `average` as {average}, you have to provide the number of classes.")
if num_classes and ignore_index is not None and (not 0 <= ignore_index < num_classes or num_classes == 1):
raise ValueError(f"The `ignore_index` {ignore_index} is not valid for inputs with {num_classes} classes")
reduce = "macro" if average in ["weighted", "none", None] else average
tp, fp, tn, fn = _stat_scores_update(
preds,
target,
reduce=reduce,
mdmc_reduce=mdmc_average,
threshold=threshold,
num_classes=num_classes,
top_k=top_k,
is_multiclass=is_multiclass,
ignore_index=ignore_index,
)
return _recall_compute(tp, fp, tn, fn, average, mdmc_average)
def precision_recall(
preds: torch.Tensor,
target: torch.Tensor,
average: str = "micro",
mdmc_average: Optional[str] = None,
ignore_index: Optional[int] = None,
num_classes: Optional[int] = None,
threshold: float = 0.5,
top_k: Optional[int] = None,
is_multiclass: Optional[bool] = None,
) -> torch.Tensor:
r"""
Computes `Precision and Recall <https://en.wikipedia.org/wiki/Precision_and_recall>`_:
.. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}}
.. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}}
Where :math:`\text{TP}`m :math:`\text{FN}` and :math:`\text{FP}` represent the number
of true positives, false negatives and false positives respecitively. With the use of
``top_k`` parameter, this metric can generalize to Recall@K and Precision@K.
The reduction method (how the recall scores are aggregated) is controlled by the
``average`` parameter, and additionally by the ``mdmc_average`` parameter in the
multi-dimensional multi-class case. Accepts all inputs listed in :ref:`references/modules:input types`.
Args:
preds: Predictions from model (probabilities, or labels)
target: Ground truth values
average:
Defines the reduction that is applied. Should be one of the following:
- ``'micro'`` [default]: Calculate the metric globally, accross all samples and classes.
- ``'macro'``: Calculate the metric for each class separately, and average the
metrics accross classes (with equal weights for each class).
- ``'weighted'``: Calculate the metric for each class separately, and average the
metrics accross classes, weighting each class by its support (``tp + fn``).
- ``'none'`` or ``None``: Calculate the metric for each class separately, and return
the metric for every class.
- ``'samples'``: Calculate the metric for each sample, and average the metrics
across samples (with equal weights for each sample).
Note that what is considered a sample in the multi-dimensional multi-class case
depends on the value of ``mdmc_average``.
mdmc_average:
Defines how averaging is done for multi-dimensional multi-class inputs (on top of the
``average`` parameter). Should be one of the following:
- ``None`` [default]: Should be left unchanged if your data is not multi-dimensional
multi-class.
- ``'samplewise'``: In this case, the statistics are computed separately for each
sample on the ``N`` axis, and then averaged over samples.
The computation for each sample is done by treating the flattened extra axes ``...``
(see :ref:`references/modules:input types`) as the ``N`` dimension within the sample,
and computing the metric for the sample based on that.
- ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
(see :ref:`references/modules:input types`)
are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
ignore_index:
Integer specifying a target class to ignore. If given, this class index does not contribute
to the returned score, regardless of reduction method. If an index is ignored, and ``average=None``
or ``'none'``, the score for the ignored class will be returned as ``nan``.
num_classes:
Number of classes. Necessary for ``'macro'``, ``'weighted'`` and ``None`` average methods.
threshold:
Threshold probability value for transforming probability predictions to binary
(0,1) predictions, in the case of binary or multi-label inputs
top_k:
Number of highest probability entries for each sample to convert to 1s - relevant
only for inputs with probability predictions. If this parameter is set for multi-label
inputs, it will take precedence over ``threshold``. For (multi-dim) multi-class inputs,
this parameter defaults to 1.
Should be left unset (``None``) for inputs with label predictions.
is_multiclass:
Used only in certain special cases, where you want to treat inputs as a different type
than what they appear to be. See the parameter's
:ref:`documentation section <references/modules:using the is_multiclass parameter>`
for a more detailed explanation and examples.
Return:
The function returns a tuple with two elements: precision and recall. Their shape
depends on the ``average`` parameter
- If ``average in ['micro', 'macro', 'weighted', 'samples']``, they are a single element tensor
- If ``average in ['none', None]``, they are a tensor of shape ``(C, )``, where ``C`` stands for
the number of classes
Example:
>>> from torchmetrics.functional import precision_recall
>>> preds = torch.tensor([2, 0, 2, 1])
>>> target = torch.tensor([1, 1, 2, 0])
>>> precision_recall(preds, target, average='macro', num_classes=3)
(tensor(0.1667), tensor(0.3333))
>>> precision_recall(preds, target, average='micro')
(tensor(0.2500), tensor(0.2500))
"""
allowed_average = ["micro", "macro", "weighted", "samples", "none", None]
if average not in allowed_average:
raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
allowed_mdmc_average = [None, "samplewise", "global"]
if mdmc_average not in allowed_mdmc_average:
raise ValueError("The `mdmc_average` has to be one of {allowed_mdmc_average}, got {mdmc_average}.")
if average in ["macro", "weighted", "none", None] and (not num_classes or num_classes < 1):
raise ValueError(f"When you set `average` as {average}, you have to provide the number of classes.")
if num_classes and ignore_index is not None and (not 0 <= ignore_index < num_classes or num_classes == 1):
raise ValueError(f"The `ignore_index` {ignore_index} is not valid for inputs with {num_classes} classes")
reduce = "macro" if average in ["weighted", "none", None] else average
tp, fp, tn, fn = _stat_scores_update(
preds,
target,
reduce=reduce,
mdmc_reduce=mdmc_average,
threshold=threshold,
num_classes=num_classes,
top_k=top_k,
is_multiclass=is_multiclass,
ignore_index=ignore_index,
)
precision = _precision_compute(tp, fp, tn, fn, average, mdmc_average)
recall = _recall_compute(tp, fp, tn, fn, average, mdmc_average)
return precision, recall