feat(feature-compressor): add DINOv2 feature extraction and compression pipeline

mini-nav/tests/test_visualizer.py

@@ -0,0 +1,97 @@
+"""Tests for FeatureVisualizer module."""
+
+import os
+import tempfile
+
+import numpy as np
+import pytest
+import torch
+from feature_compressor.core.visualizer import FeatureVisualizer
+
+
+class TestFeatureVisualizer:
+    """Test suite for FeatureVisualizer class."""
+
+    def test_histogram_generation(self):
+        """Test histogram generation from features."""
+
+        viz = FeatureVisualizer()
+        features = torch.randn(20, 256)
+
+        fig = viz.plot_histogram(features, title="Test Histogram")
+
+        assert fig is not None
+        assert "Test Histogram" in fig.layout.title.text
+
+    def test_pca_2d_generation(self):
+        """Test PCA 2D scatter plot generation."""
+
+        viz = FeatureVisualizer()
+        features = torch.randn(20, 256)
+        labels = ["cat"] * 10 + ["dog"] * 10
+
+        fig = viz.plot_pca_2d(features, labels=labels)
+
+        assert fig is not None
+        assert "PCA 2D" in fig.layout.title.text
+
+    def test_comparison_plot_generation(self):
+        """Test comparison plot generation."""
+
+        viz = FeatureVisualizer()
+        features_list = [torch.randn(20, 256), torch.randn(20, 256)]
+        names = ["Set A", "Set B"]
+
+        fig = viz.plot_comparison(features_list, names)
+
+        assert fig is not None
+        assert "Comparison" in fig.layout.title.text
+
+    def test_html_export(self):
+        """Test HTML export format."""
+
+        viz = FeatureVisualizer()
+        features = torch.randn(10, 256)
+
+        fig = viz.plot_histogram(features)
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            output_path = os.path.join(tmpdir, "test_plot")
+            viz.save(fig, output_path, formats=["html"])
+
+            assert os.path.exists(output_path + ".html")
+
+    def test_png_export(self):
+        """Test PNG export format."""
+
+        viz = FeatureVisualizer()
+        features = torch.randn(10, 256)
+
+        fig = viz.plot_histogram(features)
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            output_path = os.path.join(tmpdir, "test_plot")
+
+            # Skip PNG export if Chrome not available
+            try:
+                viz.save(fig, output_path, formats=["png"])
+                assert os.path.exists(output_path + ".png")
+            except RuntimeError as e:
+                if "Chrome" in str(e):
+                    pass
+                else:
+                    raise
+
+    def test_json_export(self):
+        """Test JSON export format."""
+
+        viz = FeatureVisualizer()
+        features = torch.randn(10, 256)
+
+        fig = viz.plot_histogram(features)
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            output_path = os.path.join(tmpdir, "test_plot")
+            viz.save(fig, output_path, formats=["json"])
+
+            assert os.path.exists(output_path + ".json")