A failed attempt to adapt http://scikit-learn.org/stable/auto_examples/decomposition/plot_image_denoising.html#example-decomposition-plot-image-denoising-py to use pipelines

plot_image_denoising_pipelined.py

from time import time

import pylab as pl
import scipy as sp
import numpy as np

from sklearn.decomposition import MiniBatchDictionaryLearning
from sklearn.feature_extraction.image import PatchExtractor
from sklearn.feature_extraction.image import reconstruct_from_patches_2d
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Scaler

###############################################################################
# Load Lena image and extract patches

lena = sp.lena() / 256.0

# downsample for higher speed
lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2]
lena /= 4.0
height, width = lena.shape

# Distort the right half of the image
print 'Distorting image...'
distorted = lena.copy()
distorted[:, height / 2:] += 0.075 * np.random.randn(width, height / 2)

# Extract all clean patches from the left half of the image
print 'Extracting clean patches...'
t0 = time()
patch_size = (7, 7)


pipeline = Pipeline([("extract", PatchExtractor(patch_size)), ("scale", Scaler()), ("sparse", MiniBatchDictionaryLearning(n_atoms=100, alpha=1e-2, n_iter=500))])

data = distorted[:, :height / 2]
print "Data shape", data.shape
RESHAPE = False  # Fails for either value of RESHAPE
if RESHAPE:
    data.shape = (1, data.shape[0], data.shape[1])
    print "Data reshaped", data.shape

###############################################################################
# Learn the dictionary from clean patches

print 'Learning the dictionary... '
t0 = time()
V = pipeline.fit(data).components_
print "V shape", V.shape
dt = time() - t0
print 'done in %.2fs.' % dt