adding confusion_matrix_patch.py with descriptions

Author: congdanh2516

monai/metrics/confusion_matrix_patch.py

@@ -0,0 +1,360 @@
+# Copyright (c) MONAI Consortium
+# 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 __future__ import annotations
+
+import warnings
+from collections.abc import Sequence
+
+import torch
+
+from monai.metrics.utils import do_metric_reduction, ignore_background
+from monai.utils import MetricReduction, ensure_tuple
+
+from .metric import CumulativeIterationMetric
+
+
+class ConfusionMatrixMetricPatch(CumulativeIterationMetric):
+    """
+    Compute confusion matrix related metrics. This function supports to calculate all metrics mentioned in:
+    `Confusion matrix <https://en.wikipedia.org/wiki/Confusion_matrix>`_.
+    It can support both multi-classes and multi-labels classification and segmentation tasks.
+    `y_preds` is expected to have binarized predictions and `y` should be in one-hot format. You can use suitable transforms
+    in ``monai.transforms.post`` first to achieve binarized values.
+    The `include_background` parameter can be set to ``False`` for an instance to exclude
+    the first category (channel index 0) which is by convention assumed to be background. If the non-background
+    segmentations are small compared to the total image size they can get overwhelmed by the signal from the
+    background.
+
+    Example of the typical execution steps of this metric class follows :py:class:`monai.metrics.metric.Cumulative`.
+
+    Args:
+        include_background: whether to include metric computation on the first channel of
+            the predicted output. Defaults to True.
+        metric_name: [``"sensitivity"``, ``"specificity"``, ``"precision"``, ``"negative predictive value"``,
+            ``"miss rate"``, ``"fall out"``, ``"false discovery rate"``, ``"false omission rate"``,
+            ``"prevalence threshold"``, ``"threat score"``, ``"accuracy"``, ``"balanced accuracy"``,
+            ``"f1 score"``, ``"matthews correlation coefficient"``, ``"fowlkes mallows index"``,
+            ``"informedness"``, ``"markedness"``]
+            Some of the metrics have multiple aliases (as shown in the wikipedia page aforementioned),
+            and you can also input those names instead.
+            Except for input only one metric, multiple metrics are also supported via input a sequence of metric names, such as
+            ("sensitivity", "precision", "recall"), if ``compute_sample`` is ``True``, multiple ``f`` and ``not_nans`` will be
+            returned with the same order as input names when calling the class.
+        compute_sample: when reducing, if ``True``, each sample's metric will be computed based on each confusion matrix first.
+            if ``False``, compute reduction on the confusion matrices first, defaults to ``False``.
+        reduction: define mode of reduction to the metrics, will only apply reduction on `not-nan` values,
+            available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
+            ``"mean_channel"``, ``"sum_channel"``}, default to ``"mean"``. if "none", will not do reduction.
+        get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns [(metric, not_nans), ...]. If False,
+            aggregate() returns [metric, ...].
+            Here `not_nans` count the number of not nans for True Positive, False Positive, True Negative and False Negative.
+            Its shape depends on the shape of the metric, and it has one more dimension with size 4. For example, if the shape
+            of the metric is [3, 3], `not_nans` has the shape [3, 3, 4].
+
+    """
+
+    def __init__(
+        self,
+        include_background: bool = True,
+        metric_name: Sequence[str] | str = "hit_rate",
+        compute_sample: bool = False,
+        reduction: MetricReduction | str = MetricReduction.MEAN,
+        get_not_nans: bool = False,
+    ) -> None:
+        super().__init__()
+        self.include_background = include_background
+        self.metric_name = ensure_tuple(metric_name)
+        self.compute_sample = compute_sample
+        self.reduction = reduction
+        self.get_not_nans = get_not_nans
+
+    def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:  # type: ignore[override]
+        """
+        Args:
+            y_pred: input data to compute. It must be one-hot format and first dim is batch.
+                The values should be binarized.
+            y: ground truth to compute the metric. It must be one-hot format and first dim is batch.
+                The values should be binarized.
+        Raises:
+            ValueError: when `y_pred` has less than two dimensions.
+        """
+        # check dimension
+        dims = y_pred.ndimension()
+        if dims < 2:
+            raise ValueError("y_pred should have at least two dimensions.")
+        if dims == 2 or (dims == 3 and y_pred.shape[-1] == 1):
+            if self.compute_sample:
+                warnings.warn("As for classification task, compute_sample should be False.")
+                self.compute_sample = False
+
+        return get_confusion_matrix(y_pred=y_pred, y=y, include_background=self.include_background)
+
+    def aggregate(
+        self, compute_sample: bool = False, reduction: MetricReduction | str | None = None
+    ) -> list[torch.Tensor | tuple[torch.Tensor, torch.Tensor]]:
+        """
+        Execute reduction for the confusion matrix values.
+
+        Args:
+            compute_sample: when reducing, if ``True``, each sample's metric will be computed based on each confusion matrix first.
+                if ``False``, compute reduction on the confusion matrices first, defaults to ``False``.
+            reduction: define mode of reduction to the metrics, will only apply reduction on `not-nan` values,
+                available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
+                ``"mean_channel"``, ``"sum_channel"``}, default to `self.reduction`. if "none", will not do reduction.
+
+        """
+        data = self.get_buffer()
+        if not isinstance(data, torch.Tensor):
+            raise ValueError("the data to aggregate must be PyTorch Tensor.")
+
+        results: list[torch.Tensor | tuple[torch.Tensor, torch.Tensor]] = []
+        for metric_name in self.metric_name:
+            if compute_sample or self.compute_sample:
+                sub_confusion_matrix = compute_confusion_matrix_metric(metric_name, data)
+                f, not_nans = do_metric_reduction(sub_confusion_matrix, reduction or self.reduction)
+            else:
+                f, not_nans = do_metric_reduction(data, reduction or self.reduction)
+                f = compute_confusion_matrix_metric(metric_name, f)
+            if self.get_not_nans:
+                results.append((f, not_nans))
+            else:
+                results.append(f)
+        return results
+
+
+def get_confusion_matrix(y_pred: torch.Tensor, y: torch.Tensor, include_background: bool = True) -> torch.Tensor:
+    """
+    Compute confusion matrix. A tensor with the shape [BC4] will be returned. Where, the third dimension
+    represents the number of true positive, false positive, true negative and false negative values for
+    each channel of each sample within the input batch. Where, B equals to the batch size and C equals to
+    the number of classes that need to be computed.
+
+    Args:
+        y_pred: input data to compute. It must be one-hot format and first dim is batch.
+            The values should be binarized.
+        y: ground truth to compute the metric. It must be one-hot format and first dim is batch.
+            The values should be binarized.
+        include_background: whether to include metric computation on the first channel of
+            the predicted output. Defaults to True.
+
+    Raises:
+        ValueError: when `y_pred` and `y` have different shapes.
+    """
+
+    if not include_background:
+        y_pred, y = ignore_background(y_pred=y_pred, y=y)
+
+    if y.shape != y_pred.shape:
+        raise ValueError(f"y_pred and y should have same shapes, got {y_pred.shape} and {y.shape}.")
+
+    # get confusion matrix related metric
+    batch_size, n_class = y_pred.shape[:2]
+    # convert to [BNS], where S is the number of pixels for one sample.
+    # As for classification tasks, S equals to 1.
+    y_pred = y_pred.reshape(batch_size, n_class, -1)
+    y = y.reshape(batch_size, n_class, -1)
+    tp = (y_pred + y) == 2
+    tn = (y_pred + y) == 0
+
+    tp = tp.sum(dim=[2]).float()
+    tn = tn.sum(dim=[2]).float()
+    p = y.sum(dim=[2]).float()
+    n = y.shape[-1] - p
+
+    fn = p - tp
+    fp = n - tn
+
+    return torch.stack([tp, fp, tn, fn], dim=-1)
+
+
+"""
+    This function is used to compute confusion matrix related metric.
+
+    Args:
+        metric_name: [``"sensitivity"``, ``"specificity"``, ``"precision"``, ``"negative predictive value"``,
+            ``"miss rate"``, ``"fall out"``, ``"false discovery rate"``, ``"false omission rate"``,
+            ``"prevalence threshold"``, ``"threat score"``, ``"accuracy"``, ``"balanced accuracy"``,
+            ``"f1 score"``, ``"matthews correlation coefficient"``, ``"fowlkes mallows index"``,
+            ``"informedness"``, ``"markedness"``]
+            Some of the metrics have multiple aliases (as shown in the wikipedia page aforementioned),
+            and you can also input those names instead.
+        confusion_matrix: Please see the doc string of the function ``get_confusion_matrix`` for more details.
+
+    Raises:
+        ValueError: when the size of the last dimension of confusion_matrix is not 4.
+        NotImplementedError: when specify a not implemented metric_name.
+
+    """
+
+def compute_confusion_matrix_metric(metric_name: str, confusion_matrix: torch.Tensor) -> torch.Tensor:
+
+    metric = check_confusion_matrix_metric_name(metric_name)
+
+    """ Ckeck dimensionality of confusion_matrix tensor """
+    input_dim = confusion_matrix.ndimension()
+
+    """
+    If confusion_matrix is a one-dimensional tensor, it will be given a new dimension.
+    If the size of the last dimension of confusion_matrix is not 4 (the expected size of a standard 2x2 confusion matrix), a ValueError will be raised.
+    """
+    if input_dim == 1:
+        confusion_matrix = confusion_matrix.unsqueeze(dim=0)
+    if confusion_matrix.shape[-1] != 4:
+        raise ValueError("the size of the last dimension of confusion_matrix should be 4.")
+
+    tp = confusion_matrix[..., 0]   # get True Positive (TP) from confusion_metrix
+    fp = confusion_matrix[..., 1]   # get False Positive (FP) ...
+    tn = confusion_matrix[..., 2]   # get True Negative (TN) ...
+    fn = confusion_matrix[..., 3]   # get False Negative (FN) ...
+    p = tp + fn     # total number of actual positive cases
+    n = fp + tn     # total number of actual negative cases
+
+    # calculate metric
+    numerator: torch.Tensor
+    denominator: torch.Tensor | float
+    nan_tensor = torch.tensor(float("nan"), device=confusion_matrix.device)
+
+    """
+    1. tpr - True Positive Rate (Recall): The ratio of correctly predicted positive samples to the total number of samples that are actually positive.
+    2. tnr - True Negative Rate: The proportion of correctly predicted negative samples over the total number of samples that are actually negative.
+    3. ppv - Positive Predictive Value (Precision): The ratio of correctly predicted positive samples to the total number of samples predicted to be positive.
+    4. npv - Negative Predictive Value: The ratio of correctly predicted negative samples to the total number of samples predicted to be negative.
+    5. fnr - False Negative Rate: The ratio of positive samples that are incorrectly predicted to be negative to the total number of samples that are actually positive.
+    6. fpr - False Positive Rate: The ratio of negative samples that are incorrectly predicted as positive to the total number of samples that are actually negative.
+    7. fdr - False Discovery Rate:  The ratio of predicted positive samples that are actually negative to the total number of samples predicted to be positive.
+    8. for - False Omission Rate: The ratio of predicted negative samples that are actually positive to the total number of samples predicted to be negative.
+    9. pt - Prevalence Threshold: It provides insight into the optimal balance point for deciding a positive or negative classification based on the prevalence of the condition in the dataset.
+    10. ts - Threat Score: It measures the proportion of correct predictions among all relevant events.
+    11. acc - Accuracy:  It measures the proportion of correctly classified instances out of the total number of instances in a dataset.
+    12. ba - Balanced Accuracy: It adjusts the traditional accuracy by accounting for both the True Positive Rate (Sensitivity) and the True Negative Rate (Specificity).
+    13. f1 - F1-score: It is a performance metric for classification tasks, especially useful when the dataset is imbalanced. It combines Precision and Recall into a single metric by calculating their harmonic mean.
+    14. mcc - Matthews Correlation Coefficient: A more robust measure of correlation between prediction and observation than accuracy, especially in cases of imbalanced classes.
+    15. fm - Fowlkes-Mallows Index: It measures the geometric mean of precision and recall.
+    16. bm - Informedness: It measures the extent to which the model's predictions are better than random guessing.
+    17. mk - Markedness: It measures the extent to which the model's predictions are better than random guessing, focusing on the positive class.
+    """
+
+    match metric:
+        case "tpr":
+            numerator, denominator = tp, p
+        case "tnr": # 
+            numerator, denominator = tn, n
+        case "ppv":
+            numerator, denominator = tp, (tp + fp)
+        case "npv":
+            numerator, denominator = tn, (tn + fn)
+        case "fnr":
+            numerator, denominator = fn, p
+        case "fpr":
+            numerator, denominator = fp, n
+        case "fdr":
+            numerator, denominator = fp, (fp + tp)
+        case "for":
+            numerator, denominator = fn, (fn + tn)
+        case "pt":
+            tpr = torch.where(p > 0, tp / p, nan_tensor)
+            tnr = torch.where(n > 0, tn / n, nan_tensor)
+            numerator = torch.sqrt(tpr * (1.0 - tnr)) + tnr - 1.0
+            denominator = tpr + tnr - 1.0
+        case "ts":
+            numerator, denominator = tp, (tp + fn + fp)
+        case "acc":
+            numerator, denominator = (tp + tn), (p + n)
+        case "ba":
+            tpr = torch.where(p > 0, tp / p, nan_tensor)
+            tnr = torch.where(n > 0, tn / n, nan_tensor)
+            numerator, denominator = (tpr + tnr), 2.0
+        case "f1":
+            numerator, denominator = tp * 2.0, (tp * 2.0 + fn + fp)
+        case "mcc":
+            numerator = tp * tn - fp * fn
+            denominator = torch.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
+        case "fm":
+            tpr = torch.where(p > 0, tp / p, nan_tensor)
+            ppv = torch.where((tp + fp) > 0, tp / (tp + fp), nan_tensor)
+            numerator = torch.sqrt(ppv * tpr)
+            denominator = 1.0
+        case "bm":
+            tpr = torch.where(p > 0, tp / p, nan_tensor)
+            tnr = torch.where(n > 0, tn / n, nan_tensor)
+            numerator = tpr + tnr - 1.0
+            denominator = 1.0
+        case "mk":
+            ppv = torch.where((tp + fp) > 0, tp / (tp + fp), nan_tensor)
+            npv = torch.where((tn + fn) > 0, tn / (tn + fn), nan_tensor)
+            numerator = ppv + npv - 1.0
+            denominator = 1.0
+        case _:
+            raise NotImplementedError("the metric is not implemented.")
+
+    if isinstance(denominator, torch.Tensor):
+        return torch.where(denominator != 0, numerator / denominator, nan_tensor)
+    return numerator / denominator
+
+
+def check_confusion_matrix_metric_name(metric_name: str) -> str:
+    """
+    There are many metrics related to confusion matrix, and some of the metrics have
+    more than one names. In addition, some of the names are very long.
+    Therefore, this function is used to check and simplify the name.
+
+    Returns:
+        Simplified metric name.
+
+    Raises:
+        NotImplementedError: when the metric is not implemented.
+    """
+    metric_name = metric_name.replace(" ", "_")
+    metric_name = metric_name.lower()
+    if metric_name in ["sensitivity", "recall", "hit_rate", "true_positive_rate", "tpr"]:
+        return "tpr"
+    if metric_name in ["specificity", "selectivity", "true_negative_rate", "tnr"]:
+        return "tnr"
+    if metric_name in ["precision", "positive_predictive_value", "ppv"]:
+        return "ppv"
+    if metric_name in ["negative_predictive_value", "npv"]:
+        return "npv"
+    if metric_name in ["miss_rate", "false_negative_rate", "fnr"]:
+        return "fnr"
+    if metric_name in ["fall_out", "false_positive_rate", "fpr"]:
+        return "fpr"
+    if metric_name in ["false_discovery_rate", "fdr"]:
+        return "fdr"
+    if metric_name in ["false_omission_rate", "for"]:
+        return "for"
+    if metric_name in ["prevalence_threshold", "pt"]:
+        return "pt"
+    if metric_name in ["threat_score", "critical_success_index", "ts", "csi"]:
+        return "ts"
+    if metric_name in ["accuracy", "acc"]:
+        return "acc"
+    if metric_name in ["balanced_accuracy", "ba"]:
+        return "ba"
+    if metric_name in ["f1_score", "f1"]:
+        return "f1"
+    if metric_name in ["matthews_correlation_coefficient", "mcc"]:
+        return "mcc"
+    if metric_name in ["fowlkes_mallows_index", "fm"]:
+        return "fm"
+    if metric_name in ["informedness", "bookmaker_informedness", "bm", "youden_index", "youden"]:
+        return "bm"
+    if metric_name in ["markedness", "deltap", "mk"]:
+        return "mk"
+    raise NotImplementedError("the metric is not implemented.")
+
+
+from sklearn.metrics import confusion_matrix
+
+y_test = [0 ,1, 1, 1, 0, 1, 0, 1, 0, 1, 1]
+y_pred = [0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1]
+
+print(confusion_matrix(y_test, y_pred))