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

mini-nav/feature_compressor/utils/plot_utils.py

@@ -0,0 +1,167 @@
+"""Plotting utility functions for feature visualization."""
+
+from pathlib import Path
+from typing import List, Optional
+
+import numpy as np
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+
+def create_histogram(data: np.ndarray, title: str = None, **kwargs) -> go.Figure:
+    """Create a histogram plot.
+
+    Args:
+        data: 1D array of values
+        title: Plot title
+        **kwargs: Additional histogram arguments
+
+    Returns:
+        Plotly Figure object
+    """
+    fig = go.Figure()
+
+    fig.add_trace(
+        go.Histogram(
+            x=data.flatten(),
+            name="Feature Values",
+            **kwargs,
+        )
+    )
+
+    if title:
+        fig.update_layout(title=title)
+
+    fig.update_layout(
+        xaxis_title="Value",
+        yaxis_title="Count",
+        hovermode="x unified",
+    )
+
+    return fig
+
+
+def create_pca_scatter_2d(
+    features: np.ndarray, labels: List = None, **kwargs
+) -> go.Figure:
+    """Create a 2D PCA scatter plot.
+
+    Args:
+        features: 2D array [n_samples, n_features]
+        labels: Optional list of labels for coloring
+        **kwargs: Additional scatter arguments
+
+    Returns:
+        Plotly Figure object
+    """
+    from sklearn.decomposition import PCA
+
+    # Apply PCA
+    pca = PCA(n_components=2)
+    components = pca.fit_transform(features)
+
+    explained_var = pca.explained_variance_ratio_ * 100
+
+    fig = go.Figure()
+
+    if labels is None:
+        fig.add_trace(
+            go.Scatter(
+                x=components[:, 0],
+                y=components[:, 1],
+                mode="markers",
+                marker=dict(size=8, opacity=0.7),
+                **kwargs,
+            )
+        )
+    else:
+        for label in set(labels):
+            mask = np.array(labels) == label
+            fig.add_trace(
+                go.Scatter(
+                    x=components[mask, 0],
+                    y=components[mask, 1],
+                    mode="markers",
+                    name=str(label),
+                    marker=dict(size=8, opacity=0.7),
+                )
+            )
+
+    fig.update_layout(
+        title=f"PCA 2D Projection (Total Variance: {explained_var.sum():.1f}%)",
+        xaxis_title=f"PC 1 ({explained_var[0]:.1f}%)",
+        yaxis_title=f"PC 2 ({explained_var[1]:.1f}%)",
+        hovermode="closest",
+    )
+
+    return fig
+
+
+def create_comparison_plot(
+    features_list: List[np.ndarray], names: List[str], **kwargs
+) -> go.Figure:
+    """Create a comparison plot of multiple feature sets.
+
+    Args:
+        features_list: List of feature arrays
+        names: List of names for each feature set
+        **kwargs: Additional histogram arguments
+
+    Returns:
+        Plotly Figure object
+    """
+    fig = make_subplots(rows=1, cols=len(features_list), subplot_titles=names)
+
+    for i, features in enumerate(features_list, 1):
+        fig.add_trace(
+            go.Histogram(
+                x=features.flatten(),
+                name=names[i - 1],
+                showlegend=False,
+                **kwargs,
+            ),
+            row=1,
+            col=i,
+        )
+
+    fig.update_layout(
+        title="Feature Distribution Comparison",
+        hovermode="x unified",
+    )
+
+    return fig
+
+
+def save_figure(fig: go.Figure, path: str, format: str = "html") -> None:
+    """Save figure to file.
+
+    Args:
+        fig: Plotly Figure object
+        path: Output file path (without extension)
+        format: Output format ('html', 'png', 'json')
+    """
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+
+    if format == "html":
+        fig.write_html(str(path) + ".html", include_plotlyjs="cdn")
+    elif format == "png":
+        fig.write_image(str(path) + ".png", scale=2)
+    elif format == "json":
+        fig.write_json(str(path) + ".json")
+    else:
+        raise ValueError(f"Unsupported format: {format}")
+
+
+def apply_theme(fig: go.Figure, theme: str = "plotly_white") -> go.Figure:
+    """Apply a theme to the figure.
+
+    Args:
+        fig: Plotly Figure object
+        theme: Theme name
+
+    Returns:
+        Updated Plotly Figure object
+    """
+    fig.update_layout(template=theme)
+    return fig