refactored digits_adjust.py (dataset preprocess in cloud)
This commit is contained in:
Alexander Mordvintsev
parent
6977a89525
commit
83ccdbd0ce
@ -17,6 +17,7 @@ from common import clock, mosaic
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SZ = 20 # size of each digit is SZ x SZ
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CLASS_N = 10
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DIGITS_FN = 'digits.png'
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def load_digits(fn):
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print 'loading "%s" ...' % fn
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@ -95,7 +96,7 @@ def evaluate_model(model, digits, samples, labels):
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if __name__ == '__main__':
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print __doc__
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digits, labels = load_digits('digits.png')
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digits, labels = load_digits(DIGITS_FN)
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print 'preprocessing...'
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# shuffle digits
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@ -11,11 +11,10 @@ Usage:
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digits_adjust.py [--model {svm|knearest}] [--cloud] [--env <PiCloud environment>]
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--model {svm|knearest} - select the classifier (SVM is the default)
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--cloud - use PiCloud computing platform (for SVM only)
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--cloud - use PiCloud computing platform
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--env - cloud environment name
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'''
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# TODO dataset preprocessing in cloud
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# TODO cloud env setup tutorial
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import numpy as np
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@ -24,6 +23,14 @@ from multiprocessing.pool import ThreadPool
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from digits import *
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try:
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import cloud
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have_cloud = True
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except ImportError:
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have_cloud = False
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def cross_validate(model_class, params, samples, labels, kfold = 3, pool = None):
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n = len(samples)
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folds = np.array_split(np.arange(n), kfold)
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@ -46,66 +53,88 @@ def cross_validate(model_class, params, samples, labels, kfold = 3, pool = None)
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scores = pool.map(f, xrange(kfold))
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return np.mean(scores)
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def adjust_KNearest(samples, labels):
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print 'adjusting KNearest ...'
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best_err, best_k = np.inf, -1
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for k in xrange(1, 9):
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err = cross_validate(KNearest, dict(k=k), samples, labels)
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if err < best_err:
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best_err, best_k = err, k
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print 'k = %d, error: %.2f %%' % (k, err*100)
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best_params = dict(k=best_k)
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print 'best params:', best_params
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return best_params
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def adjust_SVM(samples, labels, usecloud=False, cloud_env=''):
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Cs = np.logspace(0, 5, 10, base=2)
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gammas = np.logspace(-7, -2, 10, base=2)
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scores = np.zeros((len(Cs), len(gammas)))
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scores[:] = np.nan
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if usecloud:
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try:
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import cloud
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except ImportError:
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print 'cloud module is not installed'
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class App(object):
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def __init__(self, usecloud=False, cloud_env=''):
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if usecloud and not have_cloud:
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print 'warning: cloud module is not installed, running locally'
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usecloud = False
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if usecloud:
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print 'uploading dataset to cloud...'
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np.savez('train.npz', samples=samples, labels=labels)
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cloud.files.put('train.npz')
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self.usecloud = usecloud
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self.cloud_env = cloud_env
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print 'adjusting SVM (may take a long time) ...'
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def f(job):
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i, j = job
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params = dict(C = Cs[i], gamma=gammas[j])
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score = cross_validate(SVM, params, samples, labels)
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return i, j, score
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def fcloud(job):
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i, j = job
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cloud.files.get('train.npz')
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npz = np.load('train.npz')
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params = dict(C = Cs[i], gamma=gammas[j])
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score = cross_validate(SVM, params, npz['samples'], npz['labels'])
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return i, j, score
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if usecloud:
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jids = cloud.map(fcloud, np.ndindex(*scores.shape), _env=cloud_env, _profile=True)
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ires = cloud.iresult(jids)
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else:
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pool = ThreadPool(processes=cv2.getNumberOfCPUs())
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ires = pool.imap_unordered(f, np.ndindex(*scores.shape))
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if self.usecloud:
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print 'uploading dataset to cloud...'
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cloud.files.put(DIGITS_FN)
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self.preprocess_job = cloud.call(self.preprocess, _env=self.cloud_env)
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else:
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self._samples, self._labels = self.preprocess()
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for count, (i, j, score) in enumerate(ires):
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scores[i, j] = score
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print '%d / %d (best error: %.2f %%, last: %.2f %%)' % (count+1, scores.size, np.nanmin(scores)*100, score*100)
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print scores
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def preprocess(self):
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if self.usecloud:
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cloud.files.get(DIGITS_FN)
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digits, labels = load_digits(DIGITS_FN)
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shuffle = np.random.permutation(len(digits))
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digits, labels = digits[shuffle], labels[shuffle]
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digits2 = map(deskew, digits)
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samples = np.float32(digits2).reshape(-1, SZ*SZ) / 255.0
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return samples, labels
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def get_dataset(self):
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if self.usecloud:
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return cloud.result(self.preprocess_job)
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else:
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return self._samples, self._labels
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def run_jobs(self, f, jobs):
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if self.usecloud:
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jids = cloud.map(f, jobs, _env=self.cloud_env, _profile=True, _depends_on=self.preprocess_job)
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ires = cloud.iresult(jids)
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else:
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pool = ThreadPool(processes=cv2.getNumberOfCPUs())
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ires = pool.imap_unordered(f, jobs)
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return ires
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def adjust_SVM(self):
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Cs = np.logspace(0, 5, 10, base=2)
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gammas = np.logspace(-7, -2, 10, base=2)
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scores = np.zeros((len(Cs), len(gammas)))
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scores[:] = np.nan
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print 'adjusting SVM (may take a long time) ...'
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def f(job):
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i, j = job
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samples, labels = self.get_dataset()
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params = dict(C = Cs[i], gamma=gammas[j])
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score = cross_validate(SVM, params, samples, labels)
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return i, j, score
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ires = self.run_jobs(f, np.ndindex(*scores.shape))
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for count, (i, j, score) in enumerate(ires):
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scores[i, j] = score
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print '%d / %d (best error: %.2f %%, last: %.2f %%)' % (count+1, scores.size, np.nanmin(scores)*100, score*100)
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print scores
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i, j = np.unravel_index(scores.argmin(), scores.shape)
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best_params = dict(C = Cs[i], gamma=gammas[j])
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print 'best params:', best_params
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print 'best error: %.2f %%' % (scores.min()*100)
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return best_params
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def adjust_KNearest(self):
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print 'adjusting KNearest ...'
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def f(k):
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samples, labels = self.get_dataset()
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err = cross_validate(KNearest, dict(k=k), samples, labels)
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return k, err
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best_err, best_k = np.inf, -1
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for k, err in self.run_jobs(f, xrange(1, 9)):
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if err < best_err:
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best_err, best_k = err, k
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print 'k = %d, error: %.2f %%' % (k, err*100)
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best_params = dict(k=best_k)
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print 'best params:', best_params, 'err: %.2f' % (best_err*100)
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return best_params
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i, j = np.unravel_index(scores.argmin(), scores.shape)
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best_params = dict(C = Cs[i], gamma=gammas[j])
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print 'best params:', best_params
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print 'best error: %.2f %%' % (scores.min()*100)
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return best_params
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if __name__ == '__main__':
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import getopt
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@ -113,6 +142,7 @@ if __name__ == '__main__':
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print __doc__
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args, _ = getopt.getopt(sys.argv[1:], '', ['model=', 'cloud', 'env='])
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args = dict(args)
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args.setdefault('--model', 'svm')
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@ -121,16 +151,10 @@ if __name__ == '__main__':
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print 'unknown model "%s"' % args['--model']
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sys.exit(1)
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digits, labels = load_digits('digits.png')
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shuffle = np.random.permutation(len(digits))
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digits, labels = digits[shuffle], labels[shuffle]
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digits2 = map(deskew, digits)
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samples = np.float32(digits2).reshape(-1, SZ*SZ) / 255.0
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t = clock()
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app = App(usecloud='--cloud' in args, cloud_env = args['--env'])
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if args['--model'] == 'knearest':
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adjust_KNearest(samples, labels)
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app.adjust_KNearest()
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else:
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adjust_SVM(samples, labels, usecloud='--cloud' in args, cloud_env = args['--env'])
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app.adjust_SVM()
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print 'work time: %f s' % (clock() - t)
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