ENH Finalize large classification method

No longer output a figure as the results as the results will instead be
discussed at greater length in the text.

Author: luispedro

ch10/figure18.py

@@ -0,0 +1,110 @@
+# This code is supporting material for the book
+# Building Machine Learning Systems with Python
+# by Willi Richert and Luis Pedro Coelho
+# published by PACKT Publishing
+#
+# It is made available under the MIT License
+
+from __future__ import print_function
+import mahotas as mh
+from glob import glob
+from sklearn import cross_validation
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.grid_search import GridSearchCV
+import numpy as np
+
+basedir = 'AnimTransDistr'
+print('This script will test classification of the AnimTransDistr dataset')
+
+C_range = 10.0 ** np.arange(-4, 3)
+grid = GridSearchCV(LogisticRegression(), param_grid={'C' : C_range})
+clf = Pipeline([('preproc', StandardScaler()),
+                ('classifier', grid)])
+
+def features_for(im):
+    from features import color_histogram
+    im = mh.imread(im)
+    img = mh.colors.rgb2grey(im).astype(np.uint8)
+    return np.concatenate([mh.features.haralick(img).ravel(),
+                                color_histogram(im)])
+
+def images():
+    '''Iterate over all (image,label) pairs
+
+    This function will return
+    '''
+    for ci, cl in enumerate(classes):
+        images = glob('{}/{}/*.jpg'.format(basedir, cl))
+        for im in sorted(images):
+            yield im, ci
+
+classes = [
+    'Anims',
+    'Cars',
+    'Distras',
+    'Trans',
+]
+
+print('Computing whole-image texture features...')
+ifeatures = []
+labels = []
+for im, ell in images():
+    ifeatures.append(features_for(im))
+    labels.append(ell)
+
+ifeatures = np.array(ifeatures)
+labels = np.array(labels)
+
+cv = cross_validation.KFold(len(ifeatures), 5, shuffle=True, random_state=123)
+scores0 = cross_validation.cross_val_score(
+    clf, ifeatures, labels, cv=cv)
+print('Accuracy (5 fold x-val) with Logistic Regression [image features]: {:.1%}'.format(
+    scores0.mean()))
+
+
+from sklearn.cluster import KMeans
+from mahotas.features import surf
+
+
+print('Computing SURF descriptors...')
+alldescriptors = []
+for im,_ in images():
+    im = mh.imread(im, as_grey=True)
+    im = im.astype(np.uint8)
+
+    # To use dense sampling, you can try the following line:
+    # alldescriptors.append(surf.dense(im, spacing=16))
+    alldescriptors.append(surf.surf(im, descriptor_only=True))
+
+print('Descriptor computation complete.')
+k = 256
+km = KMeans(k)
+
+concatenated = np.concatenate(alldescriptors)
+print('Number of descriptors: {}'.format(
+        len(concatenated)))
+concatenated = concatenated[::64]
+print('Clustering with K-means...')
+km.fit(concatenated)
+sfeatures = []
+for d in alldescriptors:
+    c = km.predict(d)
+    sfeatures.append(
+        np.array([np.sum(c == i) for i in range(k)])
+    )
+sfeatures = np.array(sfeatures, dtype=float)
+print('predicting...')
+score_SURF = cross_validation.cross_val_score(
+    clf, sfeatures, labels, cv=cv).mean()
+print('Accuracy (5 fold x-val) with Logistic Regression [SURF features]: {:.1%}'.format(
+    score_SURF.mean()))
+
+
+print('Performing classification with all features combined...')
+allfeatures = np.hstack([sfeatures, ifeatures])
+score_SURF_global = cross_validation.cross_val_score(
+    clf, allfeatures, labels, cv=cv).mean()
+print('Accuracy (5 fold x-val) with Logistic Regression [All features]: {:.1%}'.format(
+    score_SURF_global.mean()))