Release 1.2.0 (#22)

* Adding IoU

* bumping version

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "fast-stats"
-version = "1.1.0"
+version = "1.2.0"
 edition = "2021"
 
 [lib]

fast_stats/__init__.py

@@ -18,4 +18,5 @@
     binary_tp_fp_fn,
 )
 from .confusion_matrix import confusion_matrix
+from .iou import iou
 from .multiclass import f1_score, precision, recall, stats

fast_stats/iou.py

@@ -0,0 +1,52 @@
+from enum import Enum
+from typing import Union
+
+import numpy as np
+
+from ._fast_stats_ext import _binary_f1_score_reqs
+
+Result = Union[None, float]
+
+
+class ZeroDivision(Enum):
+    ZERO = "zero"
+    NONE = "none"
+
+
+def _iou(
+    tp: int, fp: int, fn: int, zero_division: ZeroDivision = ZeroDivision.NONE
+) -> Result:
+    if tp + fp + fn == 0:
+        if zero_division == ZeroDivision.NONE:
+            return None
+        elif zero_division == ZeroDivision.ZERO:
+            return 0.0
+    return tp / (tp + fp + fn)
+
+
+def iou(
+    array1: np.ndarray,
+    array2: np.ndarray,
+    zero_division: ZeroDivision = ZeroDivision.NONE,
+) -> Result:
+    """Calculation for IoU
+
+    Args:
+        array1 (np.ndarray): array of 0/1 values (must be bool or int types)
+        array2 (np.ndarray): array of 0/1 values (must be bool or int types)
+        zero_division (str): determines how to handle division by zero
+    Returns:
+        Result: None or float depending on values and zero division
+    """
+    assert array1.shape == array2.shape, "y_true and y_pred must be same shape"
+    assert all(
+        [
+            isinstance(array1, np.ndarray),
+            isinstance(array2, np.ndarray),
+        ]
+    ), "y_true and y_pred must be numpy arrays"
+    zero_division = ZeroDivision(zero_division)
+
+    tp, tp_fp, tp_fn = _binary_f1_score_reqs(array1, array2)
+    fp, fn = tp_fp - tp, tp_fn - tp
+    return _iou(tp, fp, fn, zero_division)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "fast-stats"
-version = "1.1.1"
+version = "1.2.0"
 description = "A fast and simple library for calculating basic statistics"
 readme = "README.md"
 license = {text="Apache 2.0"}

tests/test_iou.py

@@ -0,0 +1,49 @@
+import numpy as np
+import pytest
+
+import fast_stats
+
+
+@pytest.mark.parametrize(
+    "arr1,arr2,zero_division,expected",
+    [
+        (
+            np.zeros(4, dtype=np.uint64),
+            np.ones(4, dtype=np.uint64),
+            "zero",
+            0.0,
+        ),
+        (
+            np.ones(4, dtype=np.uint64),
+            np.ones(4, dtype=np.uint64),
+            "zero",
+            1.0,
+        ),
+        (
+            np.zeros(4, dtype=np.uint64),
+            np.zeros(4, dtype=np.uint64),
+            "zero",
+            0.0,
+        ),
+        (
+            np.zeros(4, dtype=np.uint64),
+            np.zeros(4, dtype=np.uint64),
+            "none",
+            None,
+        ),
+        (
+            np.ones(4, dtype=np.uint64),
+            np.array([1, 0, 0, 0], dtype=np.uint64),
+            "none",
+            0.25,
+        ),
+        (
+            np.array([1, 1, 0, 0], dtype=np.uint64),
+            np.array([1, 0, 1, 0], dtype=np.uint64),
+            "none",
+            1.0 / 3.0,
+        ),
+    ],
+)
+def test_iou(arr1, arr2, zero_division, expected):
+    assert fast_stats.iou(arr1, arr2, zero_division) == expected