user248237dfsf - 11 months ago 130

Python Question

I'm using

`dendrogram`

`scipy`

`matplotlib`

`mat = array([[1, 0.5, 0.9],`

[0.5, 1, -0.5],

[0.9, -0.5, 1]])

plt.subplot(1,2,1)

plt.title("mat")

dist_mat = mat

linkage_matrix = linkage(dist_mat,

"single")

print "linkage2:"

print linkage(1-dist_mat, "single")

dendrogram(linkage_matrix,

color_threshold=1,

labels=["a", "b", "c"],

show_leaf_counts=True)

plt.subplot(1,2,2)

plt.title("1 - mat")

dist_mat = 1 - mat

linkage_matrix = linkage(dist_mat,

"single")

dendrogram(linkage_matrix,

color_threshold=1,

labels=["a", "b", "c"],

show_leaf_counts=True)

My questions are: first, why does

`mat`

`1-mat`

`dendrogram`

finally it seems that

`show_leaf_counts`

Answer

The input to `linkage()`

is either an n x m array, representing n points in
m-dimensional space, or a one-dimensional array containing the *condensed* distance matrix. In your example, `mat`

is 3 x 3, so you are clustering
three 3-d points. Clustering is based on the distance between these points.

*Why does mat and 1-mat give identical clusterings here?*

The arrays `mat`

and `1-mat`

produce the same clustering because the clustering
is based on distances between the points, and neither a reflection (`-mat`

)
nor a translation (`mat + offset`

) of the entire data set change the relative
distances between the points.

*How can I annotate the distance along each branch of the tree using dendrogram so that the distances between pairs of nodes can be compared?*

In the code below, I
show how you can use the data returned by dendrogram to label the horizontal
segments of the diagram with the corresponding distance. The values associated
with the keys `icoord`

and `dcoord`

give the x and y coordinates of each
three-segment inverted-U of the figure. In `augmented_dendrogram`

this data
is used to add a label of the distance (i.e. y value) of each horizontal
line segment in dendrogram.

```
from scipy.cluster.hierarchy import dendrogram
import matplotlib.pyplot as plt
def augmented_dendrogram(*args, **kwargs):
ddata = dendrogram(*args, **kwargs)
if not kwargs.get('no_plot', False):
for i, d in zip(ddata['icoord'], ddata['dcoord']):
x = 0.5 * sum(i[1:3])
y = d[1]
plt.plot(x, y, 'ro')
plt.annotate("%.3g" % y, (x, y), xytext=(0, -8),
textcoords='offset points',
va='top', ha='center')
return ddata
```

For your `mat`

array, the augmented dendrogram is

So point 'a' and 'c' are 1.01 units apart, and point 'b' is 1.57 units from the cluster ['a', 'c'].

*It seems that show_leaf_counts flag is ignored, is there a way to turn it on
so that the number of objects in each class is shown?*

The flag `show_leaf_counts`

only applies when not all the original data
points are shown as leaves. For example, when `trunc_mode = "lastp"`

,
only the last `p`

nodes are show.

Here's an example with 100 points:

```
import numpy as np
from scipy.cluster.hierarchy import linkage
import matplotlib.pyplot as plt
from augmented_dendrogram import augmented_dendrogram
# Generate a random sample of `n` points in 2-d.
np.random.seed(12312)
n = 100
x = np.random.multivariate_normal([0, 0], np.array([[4.0, 2.5], [2.5, 1.4]]),
size=(n,))
plt.figure(1, figsize=(6, 5))
plt.clf()
plt.scatter(x[:, 0], x[:, 1])
plt.axis('equal')
plt.grid(True)
linkage_matrix = linkage(x, "single")
plt.figure(2, figsize=(10, 4))
plt.clf()
plt.subplot(1, 2, 1)
show_leaf_counts = False
ddata = augmented_dendrogram(linkage_matrix,
color_threshold=1,
p=6,
truncate_mode='lastp',
show_leaf_counts=show_leaf_counts,
)
plt.title("show_leaf_counts = %s" % show_leaf_counts)
plt.subplot(1, 2, 2)
show_leaf_counts = True
ddata = augmented_dendrogram(linkage_matrix,
color_threshold=1,
p=6,
truncate_mode='lastp',
show_leaf_counts=show_leaf_counts,
)
plt.title("show_leaf_counts = %s" % show_leaf_counts)
plt.show()
```

These are the points in the data set:

With `p=6`

and `trunc_mode="lastp"`

, `dendrogram`

only shows the "top"
of the dendrogram. The following shows the effect of `show_leaf_counts`

.

Source (Stackoverflow)