DOC: add dendrogram example

altair/examples/dendrogram.py

@@ -0,0 +1,143 @@
+"""
+Dendrogram of Hierarchical Clustering
+-------------------------------------
+This is a dendrogram from the result of a hierarchical clustering. It's based on the example from
+https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html
+"""
+# category: case studies
+
+import pandas as pd
+import altair as alt
+import numpy as np
+
+# the variable `den` shown below is an exemplary output of `scipy.cluster.hierarchy.dendrogram` 
+# (https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.dendrogram.html#scipy.cluster.hierarchy.dendrogram)
+# where the dendrogram itself is truncated such that no more than 3 levels of the dendrogram tree are shown.
+den = {
+    'dcoord': [[0.0, 0.8187388676087964, 0.8187388676087964, 0.0],
+  [0.0, 1.105139508538779, 1.105139508538779, 0.0],
+  [0.8187388676087964,
+   1.3712698320830048,
+   1.3712698320830048,
+   1.105139508538779],
+  [0.0, 0.9099819926189507, 0.9099819926189507, 0.0],
+  [0.0, 1.2539936203984452, 1.2539936203984452, 0.0],
+  [0.9099819926189507,
+   1.9187528699821954,
+   1.9187528699821954,
+   1.2539936203984452],
+  [1.3712698320830048,
+   3.828052620290243,
+   3.828052620290243,
+   1.9187528699821954],
+  [0.0, 1.7604450194955439, 1.7604450194955439, 0.0],
+  [0.0, 1.845844754344974, 1.845844754344974, 0.0],
+  [1.7604450194955439,
+   4.847708507921838,
+   4.847708507921838,
+   1.845844754344974],
+  [0.0, 2.8139388316471536, 2.8139388316471536, 0.0],
+  [0.0, 2.8694176394568705, 2.8694176394568705, 0.0],
+  [2.8139388316471536,
+   6.399406819518539,
+   6.399406819518539,
+   2.8694176394568705],
+  [4.847708507921838,
+   12.300396052792589,
+   12.300396052792589,
+   6.399406819518539],
+  [3.828052620290243,
+   32.44760699959244,
+   32.44760699959244,
+   12.300396052792589]],
+ 'icoord': [[5.0, 5.0, 15.0, 15.0],
+  [25.0, 25.0, 35.0, 35.0],
+  [10.0, 10.0, 30.0, 30.0],
+  [45.0, 45.0, 55.0, 55.0],
+  [65.0, 65.0, 75.0, 75.0],
+  [50.0, 50.0, 70.0, 70.0],
+  [20.0, 20.0, 60.0, 60.0],
+  [85.0, 85.0, 95.0, 95.0],
+  [105.0, 105.0, 115.0, 115.0],
+  [90.0, 90.0, 110.0, 110.0],
+  [125.0, 125.0, 135.0, 135.0],
+  [145.0, 145.0, 155.0, 155.0],
+  [130.0, 130.0, 150.0, 150.0],
+  [100.0, 100.0, 140.0, 140.0],
+  [40.0, 40.0, 120.0, 120.0]],
+ 'ivl': [
+     '(7)', '(8)', '41', '(5)', '(10)', '(7)', '(4)', '(8)', '(9)', '(15)', '(5)', '(7)', '(4)', '(22)', '(15)', '(23)'
+     ],
+}
+
+def get_leaf_loc(den):
+    """
+    Get the location of the leaves
+    """
+    _from = int(np.array(den["icoord"]).min())
+    _to = int(np.array(den["icoord"]).max() + 1)
+    return range(_from, _to, 10)
+
+def get_df_coord(den):
+    """
+    Get coordinate dataframe.
+    """
+    # if you view the dendrogram as a collection of upside-down "U" shapes, then
+    # we can regard the 4 corners of the upside-down "U" as points 1, 2, 3 and 4.
+    cols_xk = ["xk1", "xk2", "xk3", "xk4"]
+    cols_yk = ["yk1", "yk2", "yk3", "yk4"]
+
+    df_coord = pd.merge(
+        pd.DataFrame(den["icoord"], columns=cols_xk),
+        pd.DataFrame(den["dcoord"], columns=cols_yk),
+        left_index=True,
+        right_index=True
+    )
+    return df_coord
+
+source = get_df_coord(den)
+base = alt.Chart(source)
+
+# the U shape is composed of a shoulder plus two arms
+shoulder = base.mark_rule().encode(
+    alt.X("xk2:Q", title=""),
+    alt.X2("xk3:Q"),
+    alt.Y("yk2:Q", title="")
+)
+arm1 = base.mark_rule().encode(
+    alt.X("xk1:Q"), 
+    alt.Y("yk1:Q"), 
+    alt.Y2("yk2:Q")
+)
+arm2 = base.mark_rule().encode(
+    alt.X("xk3:Q"), 
+    alt.Y("yk3:Q"), 
+    alt.Y2("yk4:Q")
+)
+
+chart_den = shoulder + arm1 + arm2
+
+df_text = pd.DataFrame(dict(labels=den["ivl"], x=get_leaf_loc(den)))
+
+chart_text = alt.Chart(
+    df_text
+).mark_text(
+    dy=0, angle=0, align="center"
+).encode(
+    x = alt.X("x:Q", axis={"grid":False, "title":"Number of points in nodes"}),
+    text = alt.Text("labels:N")
+)
+
+(chart_den & chart_text).resolve_scale(
+    x="shared"
+).configure(
+    padding={"top":10,"left":10}
+).configure_concat(
+    spacing=0
+).configure_axis(
+    labels=False,
+    ticks=False,
+    grid=False
+).properties(
+    title="Hierarchical Clustering Dendrogram"
+)