update(dendrogram-basic): altair — comprehensive quality review

plots/dendrogram-basic/implementations/altair.py

@@ -1,125 +1,214 @@
 """ pyplots.ai
 dendrogram-basic: Basic Dendrogram
-Library: altair 6.0.0 | Python 3.13.11
-Quality: 93/100 | Created: 2025-12-23
+Library: altair 6.0.0 | Python 3.14.3
+Quality: 88/100 | Updated: 2026-04-05
 """
 
 import altair as alt
-import numpy as np
 import pandas as pd
-from scipy.cluster.hierarchy import dendrogram, linkage
-
-
-# Data - Iris flower measurements (4 features for 15 samples)
-np.random.seed(42)
-
-# Simulate iris-like measurements: sepal length, sepal width, petal length, petal width
-# Three species with distinct characteristics
-samples_per_species = 5
-labels = []
-data = []
-
-# Setosa: shorter petals, wider sepals
-for i in range(samples_per_species):
-    labels.append(f"Setosa-{i + 1}")
-    data.append(
-        [
-            5.0 + np.random.randn() * 0.3,  # sepal length
-            3.4 + np.random.randn() * 0.3,  # sepal width
-            1.5 + np.random.randn() * 0.2,  # petal length
-            0.3 + np.random.randn() * 0.1,  # petal width
-        ]
-    )
+from scipy.cluster.hierarchy import dendrogram, fcluster, linkage
+from sklearn.datasets import load_iris
 
-# Versicolor: medium measurements
-for i in range(samples_per_species):
-    labels.append(f"Versicolor-{i + 1}")
-    data.append(
-        [
-            5.9 + np.random.randn() * 0.4,
-            2.8 + np.random.randn() * 0.3,
-            4.3 + np.random.randn() * 0.4,
-            1.3 + np.random.randn() * 0.2,
-        ]
-    )
 
-# Virginica: longer petals and sepals
-for i in range(samples_per_species):
-    labels.append(f"Virginica-{i + 1}")
-    data.append(
-        [
-            6.6 + np.random.randn() * 0.5,
-            3.0 + np.random.randn() * 0.3,
-            5.5 + np.random.randn() * 0.5,
-            2.0 + np.random.randn() * 0.3,
-        ]
+# Data - Iris flower measurements (15 samples, 3 species)
+iris = load_iris()
+indices = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140]
+features = iris.data[indices]
+species_names = ["Setosa", "Versicolor", "Virginica"]
+labels = [f"{species_names[iris.target[i]]}-{i}" for i in indices]
+
+# Compute hierarchical clustering using Ward's method
+Z = linkage(features, method="ward")
+dendro = dendrogram(Z, labels=labels, no_plot=True)
+
+# Assign cluster colors based on distance threshold
+distance_threshold = 5.0
+cluster_ids = fcluster(Z, t=distance_threshold, criterion="distance")
+cluster_colors = {1: "#306998", 2: "#D4A017", 3: "#7B68AE"}
+
+# Build a mapping from leaf index to cluster color, then propagate to merges
+n_leaves = len(labels)
+node_colors = {}
+for idx in dendro["leaves"]:
+    node_colors[idx] = cluster_colors.get(cluster_ids[idx], "#888888")
+
+# Track merged node colors through linkage
+for i, row in enumerate(Z):
+    left, right = int(row[0]), int(row[1])
+    left_c = node_colors.get(left, "#888888")
+    right_c = node_colors.get(right, "#888888")
+    node_colors[n_leaves + i] = left_c if left_c == right_c else "#888888"
+
+# Extract line segments with cluster-based coloring
+segments = []
+for merge_idx, (xpts, ypts) in enumerate(zip(dendro["icoord"], dendro["dcoord"], strict=True)):
+    merge_height = max(ypts)
+    left_node = int(Z[merge_idx, 0])
+    right_node = int(Z[merge_idx, 1])
+    left_c = node_colors.get(left_node, "#888888")
+    right_c = node_colors.get(right_node, "#888888")
+    merge_c = left_c if left_c == right_c else "#888888"
+
+    # Left vertical
+    segments.append(
+        {"x": xpts[0], "y": ypts[0], "x2": xpts[1], "y2": ypts[1], "color": left_c, "distance": round(merge_height, 2)}
+    )
+    # Horizontal bar
+    segments.append(
+        {"x": xpts[1], "y": ypts[1], "x2": xpts[2], "y2": ypts[2], "color": merge_c, "distance": round(merge_height, 2)}
+    )
+    # Right vertical
+    segments.append(
+        {"x": xpts[2], "y": ypts[2], "x2": xpts[3], "y2": ypts[3], "color": right_c, "distance": round(merge_height, 2)}
     )
 
-data = np.array(data)
-n_samples = len(labels)
+segments_df = pd.DataFrame(segments)
+
+# Leaf label positions from scipy (positioned at 5, 15, 25, ...)
+leaf_labels = dendro["ivl"]
+leaf_df = pd.DataFrame(
+    {
+        "x": [5 + 10 * i for i in range(len(leaf_labels))],
+        "y_base": [0.0] * len(leaf_labels),
+        "label": leaf_labels,
+        "species": [lbl.rsplit("-", 1)[0] for lbl in leaf_labels],
+    }
+)
 
-# Compute hierarchical clustering using Ward's method
-Z = linkage(data, method="ward")
+# Species color palette starting with Python Blue
+species_palette = {"Setosa": "#306998", "Versicolor": "#D4A017", "Virginica": "#7B68AE"}
 
-# Use scipy's dendrogram function to get proper leaf ordering and coordinates
-dendro = dendrogram(Z, labels=labels, no_plot=True)
+# Axis domain
+x_min = min(s["x"] for s in segments) - 8
+x_max = max(s["x2"] for s in segments) + 8
+y_max = Z[:, 2].max() * 1.15
 
-# Extract coordinates from scipy's dendrogram output (icoord, dcoord)
-# Each cluster merge has 4 x-coords and 4 y-coords forming a U-shape
-lines_data = []
-color_threshold = 0.7 * Z[:, 2].max()
-
-for xpts, ypts in zip(dendro["icoord"], dendro["dcoord"], strict=True):
-    # Each U-shape has 3 segments: left vertical, horizontal, right vertical
-    # Points: (x0,y0) - (x1,y1) - (x2,y2) - (x3,y3)
-    max_height = max(ypts)
-    color = "#306998" if max_height > color_threshold else "#FFD43B"
-
-    # Left vertical segment
-    lines_data.append({"x": xpts[0], "y": ypts[0], "x2": xpts[1], "y2": ypts[1], "color": color})
-    # Horizontal segment
-    lines_data.append({"x": xpts[1], "y": ypts[1], "x2": xpts[2], "y2": ypts[2], "color": color})
-    # Right vertical segment
-    lines_data.append({"x": xpts[2], "y": ypts[2], "x2": xpts[3], "y2": ypts[3], "color": color})
-
-lines_df = pd.DataFrame(lines_data)
-
-# Create label data for x-axis using scipy's leaf positions
-ivl = dendro["ivl"]  # Ordered labels from dendrogram
-# Labels are positioned at 5, 15, 25, ... (5 + 10*i)
-label_positions = [5 + 10 * i for i in range(len(ivl))]
-label_data = pd.DataFrame({"x": label_positions, "label": ivl})
-
-# Get x-axis domain from the dendrogram coordinates
-x_min = min(min(xpts) for xpts in dendro["icoord"]) - 5
-x_max = max(max(xpts) for xpts in dendro["icoord"]) + 5
-
-# Create the dendrogram lines chart
-dendrogram_lines = (
-    alt.Chart(lines_df)
+# Annotation for the final merge (top of tree) — key storytelling element
+top_merge_y = Z[-1, 2]
+top_merge_x = (dendro["icoord"][-1][1] + dendro["icoord"][-1][2]) / 2
+annotation_df = pd.DataFrame(
+    {"x": [top_merge_x], "y": [top_merge_y], "text": ["Setosa diverges\nfrom Versicolor + Virginica"]}
+)
+
+# Interactive selection: click legend to highlight a species
+species_selection = alt.selection_point(fields=["species"], bind="legend")
+
+# Dendrogram branches with cluster-based coloring and tooltips
+branches = (
+    alt.Chart(segments_df)
     .mark_rule(strokeWidth=3)
     .encode(
-        x=alt.X("x:Q", axis=None, scale=alt.Scale(domain=[x_min, x_max])),
+        x=alt.X("x:Q", scale=alt.Scale(domain=[x_min, x_max]), axis=None),
         x2="x2:Q",
-        y=alt.Y("y:Q", title="Distance (Ward)", scale=alt.Scale(domain=[0, Z[:, 2].max() * 1.1])),
+        y=alt.Y("y:Q", title="Distance (Ward's method)", scale=alt.Scale(domain=[0, y_max])),
         y2="y2:Q",
         color=alt.Color("color:N", scale=None),
+        tooltip=[alt.Tooltip("distance:Q", title="Merge Distance", format=".2f")],
+    )
+)
+
+# Leaf markers at base of dendrogram colored by species
+leaf_dots = (
+    alt.Chart(leaf_df)
+    .mark_point(size=180, filled=True, strokeWidth=1.5, stroke="white")
+    .encode(
+        x=alt.X("x:Q", scale=alt.Scale(domain=[x_min, x_max]), axis=None),
+        y=alt.Y("y_base:Q", scale=alt.Scale(domain=[0, y_max])),
+        color=alt.Color(
+            "species:N",
+            scale=alt.Scale(domain=list(species_palette.keys()), range=list(species_palette.values())),
+            legend=alt.Legend(
+                title="Species",
+                titleFontSize=18,
+                titleFontWeight="bold",
+                labelFontSize=16,
+                symbolSize=220,
+                orient="right",
+                offset=10,
+                titleColor="#333333",
+                labelColor="#444444",
+            ),
+        ),
+        tooltip=[alt.Tooltip("label:N", title="Sample"), alt.Tooltip("species:N", title="Species")],
+        opacity=alt.condition(species_selection, alt.value(1.0), alt.value(0.15)),
+    )
+    .add_params(species_selection)
+)
+
+# Leaf labels colored by species
+leaf_text = (
+    alt.Chart(leaf_df)
+    .mark_text(angle=315, align="right", baseline="top", fontSize=16, fontWeight="bold", dx=-4, dy=4)
+    .encode(
+        x=alt.X("x:Q", scale=alt.Scale(domain=[x_min, x_max]), axis=None),
+        y=alt.value(870),
+        text="label:N",
+        color=alt.Color(
+            "species:N",
+            scale=alt.Scale(domain=list(species_palette.keys()), range=list(species_palette.values())),
+            legend=None,
+        ),
+        opacity=alt.condition(species_selection, alt.value(1.0), alt.value(0.15)),
     )
 )
 
-# Create x-axis labels at bottom
-x_labels = (
-    alt.Chart(label_data)
-    .mark_text(angle=315, align="right", baseline="top", fontSize=14)
-    .encode(x=alt.X("x:Q", axis=None, scale=alt.Scale(domain=[x_min, x_max])), y=alt.value(850), text="label:N")
+# Distance threshold reference line
+threshold_df = pd.DataFrame({"y": [distance_threshold]})
+threshold_line = (
+    alt.Chart(threshold_df).mark_rule(strokeDash=[8, 6], strokeWidth=1.8, color="#CC4444", opacity=0.7).encode(y="y:Q")
 )
 
-# Combine charts
+threshold_label = (
+    alt.Chart(threshold_df)
+    .mark_text(align="left", baseline="bottom", fontSize=14, color="#CC4444", fontStyle="italic", dx=5, dy=-5)
+    .encode(x=alt.value(10), y="y:Q", text=alt.value("cluster threshold (d = 5.0)"))
+)
+
+# Annotation at top merge point
+top_annotation = (
+    alt.Chart(annotation_df)
+    .mark_text(align="left", baseline="middle", fontSize=14, fontWeight="bold", color="#555555", lineBreak="\n", dx=12)
+    .encode(x="x:Q", y="y:Q", text="text:N")
+)
+
+top_arrow = (
+    alt.Chart(annotation_df)
+    .mark_point(shape="triangle-left", size=80, filled=True, color="#888888")
+    .encode(x="x:Q", y="y:Q")
+)
+
+# Combine layers
 chart = (
-    alt.layer(dendrogram_lines, x_labels)
-    .properties(width=1600, height=900, title=alt.Title("dendrogram-basic · altair · pyplots.ai", fontSize=28))
-    .configure_axis(labelFontSize=18, titleFontSize=22, gridOpacity=0.3, gridDash=[4, 4])
-    .configure_view(strokeWidth=0)
+    alt.layer(branches, threshold_line, threshold_label, leaf_dots, leaf_text, top_arrow, top_annotation)
+    .properties(
+        width=1600,
+        height=900,
+        title=alt.Title(
+            "dendrogram-basic · altair · pyplots.ai",
+            subtitle="Ward's linkage on Iris measurements — Setosa separates clearly from Versicolor / Virginica",
+            fontSize=28,
+            subtitleFontSize=18,
+            subtitleColor="#666666",
+            anchor="start",
+            offset=20,
+        ),
+    )
+    .configure_axis(
+        labelFontSize=18,
+        titleFontSize=22,
+        titleColor="#333333",
+        labelColor="#555555",
+        gridOpacity=0.12,
+        gridDash=[3, 5],
+        gridColor="#cccccc",
+        domainColor="#aaaaaa",
+        domainWidth=1.5,
+        tickColor="#bbbbbb",
+        tickSize=6,
+    )
+    .configure_view(strokeWidth=0, fill="#FAFBFC")
+    .configure_legend(padding=20, cornerRadius=6, strokeColor="#dddddd", fillColor="#FAFBFC")
+    .configure_title(subtitlePadding=8)
 )
 
 # Save