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Visualization in the Chen and Church example #1

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Visualization in the Chen and Church example #1

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28 changes: 19 additions & 9 deletions examples/bicluster/plot_cheng_church.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@
# License: BSD 3 clause

from matplotlib import pyplot as plt
import numpy as np

from sklearn.datasets import make_msr_biclusters
from sklearn.datasets import samples_generator as sg
Expand All @@ -30,14 +31,30 @@
shuffle=False,
random_state=0)

data, row_idx, col_idx = sg._shuffle(data, random_state=0)

# Fit the biclustering model
model = ChengChurch(n_clusters=3, max_msr=100, random_state=0)
model.fit(data)
score = consensus_score(model.biclusters_,
(rows[:, row_idx], columns[:, col_idx]))

print "consensus score: {:.1f}".format(score)

# Plot the affinity matrix

plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Original dataset")

data, row_idx, col_idx = sg._shuffle(data, random_state=0)

plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Shuffled dataset")

row_order = np.argsort(np.argmax(model.rows_, axis=0), kind='mergesort')
column_order = np.argsort(np.argmax(model.columns_, axis=0), kind='mergesort')
plt.matshow(data[row_order].T[column_order].T[:300], cmap=plt.cm.Blues)
plt.title("Reordered dataset using biclustering")

# Plot profiles
plt.figure()
n_cols = data.shape[1]
for row in data:
Expand All @@ -46,13 +63,6 @@
plt.xlabel('column numbers')
plt.ylabel('value')

model = ChengChurch(n_clusters=3, max_msr=100, random_state=0)
model.fit(data)
score = consensus_score(model.biclusters_,
(rows[:, row_idx], columns[:, col_idx]))

print "consensus score: {:.1f}".format(score)


plt.figure()
bicluster = model.get_submatrix(0, data)
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