From 13b30d7428001e6b93248c7c25c0e48cb7187b3d Mon Sep 17 00:00:00 2001
From: Alexander Mordvintsev <no@email>
Date: Wed, 27 Jun 2012 05:51:24 +0000
Subject: [PATCH] digits_adjust.py sample added

---
 samples/python2/digits_adjust.py | 136 +++++++++++++++++++++++++++++++
 1 file changed, 136 insertions(+)
 create mode 100644 samples/python2/digits_adjust.py

diff --git a/samples/python2/digits_adjust.py b/samples/python2/digits_adjust.py
new file mode 100644
index 0000000000..3e1b6b76e6
--- /dev/null
+++ b/samples/python2/digits_adjust.py
@@ -0,0 +1,136 @@
+'''
+Digit recognition adjustment. 
+Grid search is used to find the best parameters for SVN and KNearest classifiers.
+SVM adjustment follows the guidelines given in 
+http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
+
+Threading or cloud computing (with http://www.picloud.com/)) may be used 
+to speedup the computation.
+
+Usage:
+  digits_adjust.py [--model {svm|knearest}] [--cloud] [--env <PiCloud environment>]
+  
+  --model {svm|knearest}   - select the classifier (SVM is the default)
+  --cloud                  - use PiCloud computing platform (for SVM only)
+  --env                    - cloud environment name
+
+'''
+# TODO dataset preprocessing in cloud
+# TODO cloud env setup tutorial
+
+import numpy as np
+import cv2
+from multiprocessing.pool import ThreadPool
+
+from digits import *
+
+def cross_validate(model_class, params, samples, labels, kfold = 3, pool = None):
+    n = len(samples)
+    folds = np.array_split(np.arange(n), kfold)
+    def f(i):
+        model = model_class(**params)
+        test_idx = folds[i]
+        train_idx = list(folds)
+        train_idx.pop(i)
+        train_idx = np.hstack(train_idx)
+        train_samples, train_labels = samples[train_idx], labels[train_idx]
+        test_samples, test_labels = samples[test_idx], labels[test_idx]
+        model.train(train_samples, train_labels)
+        resp = model.predict(test_samples)
+        score = (resp != test_labels).mean()
+        print ".",
+        return score
+    if pool is None:
+        scores = map(f, xrange(kfold))
+    else:
+        scores = pool.map(f, xrange(kfold))
+    return np.mean(scores)
+
+def adjust_KNearest(samples, labels):
+    print 'adjusting KNearest ...'
+    best_err, best_k = np.inf, -1
+    for k in xrange(1, 9):
+        err = cross_validate(KNearest, dict(k=k), samples, labels)
+        if err < best_err:
+            best_err, best_k = err, k
+        print 'k = %d, error: %.2f %%' % (k, err*100)
+    best_params = dict(k=best_k)
+    print 'best params:', best_params
+    return best_params
+
+def adjust_SVM(samples, labels, usecloud=False, cloud_env=''):
+    Cs = np.logspace(0, 5, 10, base=2)
+    gammas = np.logspace(-7, -2, 10, base=2)
+    scores = np.zeros((len(Cs), len(gammas)))
+    scores[:] = np.nan
+
+    if usecloud:
+        try: 
+            import cloud
+        except ImportError: 
+            print 'cloud module is not installed'
+            usecloud = False
+    if usecloud:
+        print 'uploading dataset to cloud...'
+        np.savez('train.npz', samples=samples, labels=labels)
+        cloud.files.put('train.npz')
+
+    print 'adjusting SVM (may take a long time) ...'
+    def f(job):
+        i, j = job
+        params = dict(C = Cs[i], gamma=gammas[j])
+        score = cross_validate(SVM, params, samples, labels)
+        return i, j, score
+    def fcloud(job):
+        i, j = job
+        cloud.files.get('train.npz')
+        npz = np.load('train.npz')
+        params = dict(C = Cs[i], gamma=gammas[j])
+        score = cross_validate(SVM, params, npz['samples'], npz['labels'])
+        return i, j, score
+    
+    if usecloud:
+        jids = cloud.map(fcloud, np.ndindex(*scores.shape), _env=cloud_env, _profile=True)
+        ires = cloud.iresult(jids)
+    else:
+        pool = ThreadPool(processes=cv2.getNumberOfCPUs())
+        ires = pool.imap_unordered(f, np.ndindex(*scores.shape))
+
+    for count, (i, j, score) in enumerate(ires):
+        scores[i, j] = score
+        print '%d / %d (best error: %.2f %%, last: %.2f %%)' % (count+1, scores.size, np.nanmin(scores)*100, score*100)
+    print scores
+
+    i, j = np.unravel_index(scores.argmin(), scores.shape)
+    best_params = dict(C = Cs[i], gamma=gammas[j])
+    print 'best params:', best_params
+    print 'best error: %.2f %%' % (scores.min()*100)
+    return best_params
+
+if __name__ == '__main__':
+    import getopt
+    import sys
+    
+    print __doc__
+
+    args, _ = getopt.getopt(sys.argv[1:], '', ['model=', 'cloud', 'env='])
+    args = dict(args)
+    args.setdefault('--model', 'svm')
+    args.setdefault('--env', '')
+    if args['--model'] not in ['svm', 'knearest']:
+        print 'unknown model "%s"' % args['--model']
+        sys.exit(1)
+
+    digits, labels = load_digits('digits.png')
+    shuffle = np.random.permutation(len(digits))
+    digits, labels = digits[shuffle], labels[shuffle]
+    digits2 = map(deskew, digits)
+    samples = np.float32(digits2).reshape(-1, SZ*SZ) / 255.0
+    
+    t = clock()
+    if args['--model'] == 'knearest':
+        adjust_KNearest(samples, labels)
+    else:
+        adjust_SVM(samples, labels, usecloud='--cloud' in args, cloud_env = args['--env'])
+    print 'work time: %f s' % (clock() - t)
+        
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