using for loop to install conda package

.CondaPkg/env/Lib/site-packages/skimage/future/tests/test_trainable_segmentation.py

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+from functools import partial
+
+import numpy as np
+import pytest
+from scipy import spatial
+
+from skimage.future import fit_segmenter, predict_segmenter, TrainableSegmenter
+from skimage.feature import multiscale_basic_features
+
+
+class DummyNNClassifier:
+    def fit(self, X, labels):
+        self.X = X
+        self.labels = labels
+        self.tree = spatial.cKDTree(self.X)
+
+    def predict(self, X):
+        # mimic check in scikit-learn for number of features
+        if X.shape[1] != self.X.shape[1]:
+            raise ValueError(
+                f"Expected {self.X.shape[1]} features but got {X.shape[1]}."
+            )
+        nearest_neighbors = self.tree.query(X)[1]
+        return self.labels[nearest_neighbors]
+
+
+def test_trainable_segmentation_singlechannel():
+    img = np.zeros((20, 20))
+    img[:10] = 1
+    img += 0.05 * np.random.randn(*img.shape)
+    labels = np.zeros_like(img, dtype=np.uint8)
+    labels[:2] = 1
+    labels[-2:] = 2
+    clf = DummyNNClassifier()
+    features_func = partial(
+        multiscale_basic_features,
+        edges=False,
+        texture=False,
+        sigma_min=0.5,
+        sigma_max=2,
+    )
+    features = features_func(img)
+    clf = fit_segmenter(labels, features, clf)
+    out = predict_segmenter(features, clf)
+    assert np.all(out[:10] == 1)
+    assert np.all(out[10:] == 2)
+
+
+def test_trainable_segmentation_multichannel():
+    img = np.zeros((20, 20, 3))
+    img[:10] = 1
+    img += 0.05 * np.random.randn(*img.shape)
+    labels = np.zeros_like(img[..., 0], dtype=np.uint8)
+    labels[:2] = 1
+    labels[-2:] = 2
+    clf = DummyNNClassifier()
+    features = multiscale_basic_features(
+        img,
+        edges=False,
+        texture=False,
+        sigma_min=0.5,
+        sigma_max=2,
+        channel_axis=-1,
+    )
+    clf = fit_segmenter(labels, features, clf)
+    out = predict_segmenter(features, clf)
+    assert np.all(out[:10] == 1)
+    assert np.all(out[10:] == 2)
+
+
+def test_trainable_segmentation_predict():
+    img = np.zeros((20, 20))
+    img[:10] = 1
+    img += 0.05 * np.random.randn(*img.shape)
+    labels = np.zeros_like(img, dtype=np.uint8)
+    labels[:2] = 1
+    labels[-2:] = 2
+    clf = DummyNNClassifier()
+    features_func = partial(
+        multiscale_basic_features,
+        edges=False,
+        texture=False,
+        sigma_min=0.5,
+        sigma_max=2,
+    )
+    features = features_func(img)
+    clf = fit_segmenter(labels, features, clf)
+
+    test_features = np.random.random((5, 20, 20))
+    with pytest.raises(ValueError) as err:
+        _ = predict_segmenter(test_features, clf)
+        assert 'type of features' in str(err.value)
+
+
+def test_trainable_segmentation_oo():
+    """Test the object-oriented interface using the TrainableSegmenter class."""
+
+    img = np.zeros((20, 20))
+    img[:10] = 1
+    img += 0.05 * np.random.randn(*img.shape)
+    labels = np.zeros_like(img, dtype=np.uint8)
+    labels[:2] = 1
+    labels[-2:] = 2
+    clf = DummyNNClassifier()
+    features_func = partial(
+        multiscale_basic_features,
+        edges=False,
+        texture=False,
+        sigma_min=0.5,
+        sigma_max=2,
+    )
+    segmenter = TrainableSegmenter(clf=clf, features_func=features_func)
+    segmenter.fit(img, labels)
+
+    # model has been fitted
+    np.testing.assert_array_almost_equal(clf.labels, labels[labels > 0])
+
+    out = segmenter.predict(img)
+    assert np.all(out[:10] == 1)
+    assert np.all(out[10:] == 2)
+
+    # test multichannel model
+    img_with_channels = np.stack((img, img.T), axis=-1)
+    features_func = partial(
+        multiscale_basic_features,
+        channel_axis=-1,
+    )
+    segmenter = TrainableSegmenter(clf=clf, features_func=features_func)
+    segmenter.fit(img_with_channels, labels)
+
+    # model has been fitted
+    np.testing.assert_array_almost_equal(clf.labels, labels[labels > 0])
+
+    out = segmenter.predict(img_with_channels)
+    assert np.all(out[:10] == 1)
+    assert np.all(out[10:] == 2)
+
+    # test wrong number of dimensions
+    with pytest.raises(ValueError):
+        segmenter.predict(np.expand_dims(img_with_channels, axis=-1))
+
+    # test wrong number of channels
+    with pytest.raises(ValueError):
+        segmenter.predict(np.concatenate([img_with_channels] * 2, axis=-1))