141 lines
4.3 KiB
Python
141 lines
4.3 KiB
Python
|
#!/usr/bin/env python
|
||
|
|
|
||
|
|
'''
|
||
|
|
Digit recognition adjustment.
|
||
|
|
Grid search is used to find the best parameters for SVM and KNearest classifiers.
|
||
|
|
SVM adjustment follows the guidelines given in
|
||
|
|
http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
|
||
|
|
|
||
|
|
Usage:
|
||
|
|
digits_adjust.py [--model {svm|knearest}]
|
||
|
|
|
||
|
|
--model {svm|knearest} - select the classifier (SVM is the default)
|
||
|
|
|
||
|
|
'''
|
||
|
|
|
||
|
|
# Python 2/3 compatibility
|
||
|
|
from __future__ import print_function
|
||
|
|
import sys
|
||
|
|
PY3 = sys.version_info[0] == 3
|
||
|
|
|
||
|
|
if PY3:
|
||
|
|
xrange = range
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import cv2 as cv
|
||
|
|
|
||
|
|
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(".", end='')
|
||
|
|
return score
|
||
|
|
if pool is None:
|
||
|
|
scores = list(map(f, xrange(kfold)))
|
||
|
|
else:
|
||
|
|
scores = pool.map(f, xrange(kfold))
|
||
|
|
return np.mean(scores)
|
||
|
|
|
||
|
|
|
||
|
|
class App(object):
|
||
|
|
def __init__(self):
|
||
|
|
self._samples, self._labels = self.preprocess()
|
||
|
|
|
||
|
|
def preprocess(self):
|
||
|
|
digits, labels = load_digits(DIGITS_FN)
|
||
|
|
shuffle = np.random.permutation(len(digits))
|
||
|
|
digits, labels = digits[shuffle], labels[shuffle]
|
||
|
|
digits2 = list(map(deskew, digits))
|
||
|
|
samples = preprocess_hog(digits2)
|
||
|
|
return samples, labels
|
||
|
|
|
||
|
|
def get_dataset(self):
|
||
|
|
return self._samples, self._labels
|
||
|
|
|
||
|
|
def run_jobs(self, f, jobs):
|
||
|
|
pool = ThreadPool(processes=cv.getNumberOfCPUs())
|
||
|
|
ires = pool.imap_unordered(f, jobs)
|
||
|
|
return ires
|
||
|
|
|
||
|
|
def adjust_SVM(self):
|
||
|
|
Cs = np.logspace(0, 10, 15, base=2)
|
||
|
|
gammas = np.logspace(-7, 4, 15, base=2)
|
||
|
|
scores = np.zeros((len(Cs), len(gammas)))
|
||
|
|
scores[:] = np.nan
|
||
|
|
|
||
|
|
print('adjusting SVM (may take a long time) ...')
|
||
|
|
def f(job):
|
||
|
|
i, j = job
|
||
|
|
samples, labels = self.get_dataset()
|
||
|
|
params = dict(C = Cs[i], gamma=gammas[j])
|
||
|
|
score = cross_validate(SVM, params, samples, labels)
|
||
|
|
return i, j, score
|
||
|
|
|
||
|
|
ires = self.run_jobs(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)
|
||
|
|
|
||
|
|
print('writing score table to "svm_scores.npz"')
|
||
|
|
np.savez('svm_scores.npz', scores=scores, Cs=Cs, gammas=gammas)
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
def adjust_KNearest(self):
|
||
|
|
print('adjusting KNearest ...')
|
||
|
|
def f(k):
|
||
|
|
samples, labels = self.get_dataset()
|
||
|
|
err = cross_validate(KNearest, dict(k=k), samples, labels)
|
||
|
|
return k, err
|
||
|
|
best_err, best_k = np.inf, -1
|
||
|
|
for k, err in self.run_jobs(f, xrange(1, 9)):
|
||
|
|
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, 'err: %.2f' % (best_err*100))
|
||
|
|
return best_params
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
import getopt
|
||
|
|
import sys
|
||
|
|
|
||
|
|
print(__doc__)
|
||
|
|
|
||
|
|
args, _ = getopt.getopt(sys.argv[1:], '', ['model='])
|
||
|
|
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)
|
||
|
|
|
||
|
|
t = clock()
|
||
|
|
app = App()
|
||
|
|
if args['--model'] == 'knearest':
|
||
|
|
app.adjust_KNearest()
|
||
|
|
else:
|
||
|
|
app.adjust_SVM()
|
||
|
|
print('work time: %f s' % (clock() - t))
|